Antecedents of Real Estate Investment Intention among Filipino Millennials and Gen Z: An Extended Theory of Planned Behavior

Abstract

The Philippines’ real estate (RE) market vis à vis its government’s collective efforts to accelerate its digital transformation faces prevalent illegal RE practices online on top of limited publicly accessible data for decision-making and decentralized and highly regional RE markets. As the middle-income millennials’ and Gen Zs’ purchasing power rises, these increasingly important consumer groups might fall prey to online RE colorums or make bad RE investment decisions. In anticipation of big data, ML, and AI becoming integral to the Philippine RE industry, this study extends the theory of the planned behavior model to account for RE investment and illegal practice and to build a theoretical basis for foundational models. A total of 400 Filipinos aged 15 to 42 from different regions in the country responded to a self-administered online survey questionnaire. The model was assessed using partial least squares structural equation modeling (PLS-SEM) and was proven to be sufficient to explain the proposed model. Results from the partial least squares structural equation modeling (PLS-SEM) revealed that during inflation, risk tolerance (RT), perceived property value (PPV), and aversion from illegal practice (IP) significantly influence millennials’ and Gen Zs’ intention to invest in the RE market, implying that they would consider prioritizing profitability at the expense of sustainability. Thus, a collective commitment to provide transparent and real-time data on RE KPIs and projects is necessary for safer and optimized operations while ensuring the sustainability of current and future projects.

1. Introduction

The housing market in the Philippines is looking bleak mid- and post-COVID-19 pandemic for the common Filipino [1]. The forced work-from-home (WFH) arrangements and shift to the digital landscape have redefined people’s behavior and preferences in living and work/office spaces. As a result, residential property prices have seen a 27.1% growth in the second quarter of 2020—the height of the pandemic—in which there was a rise in property demand for affordable housing in general and in suburban residential areas.

The Philippine Development Plan (PDP) 2023–2028 reported that the estimated housing backlog has accumulated to 6.8 million within the Duterte administration (2017 to 2022) due to issues in affordability [2]. According to the Philippine Institute for Development Studies (PIDS), housing in the Philippines is inaccessible to millions of households, with the most affected located in highly urbanized cities [3], due to insufficient income generated by most Filipino households compared to the rising home and rental prices.

The high cost of housing in the Philippines and its inaccessibility to millions of households has been a topic of interest in the academic literature. According to a study by Abad et al. [4], housing affordability in the Philippines is impacted by a few factors such as the supply and demand dynamics of the housing market, construction costs, and land use regulations. A lack of access to financing options and high and rising interest rates were also identified as significant barriers to homeownership for low-income families. According to a study by Wulandari et al. [5], the average price of housing units in metro Manila increased by 27% between 2015 and 2020, making it difficult for low- and middle-income families to afford decent housing. Similarly, the study attributes the increase in housing prices to several factors, including land scarcity, high construction costs, and rising housing demand.

The high cost of housing in the Philippines has significant social and economic implications, including increasing inequality and limiting economic growth. Corporations and other developers, who are focused on catering to rich households that can afford smarter home offices and generally higher-quality homes, further divide the people [3,6,7,8]. The real estate sector is even booming despite the challenges previously stated, and has proven to be strong even in a recession [9]. Hence, addressing this issue will require a multi-faceted approach that includes increasing the supply of affordable housing, improving access to financing, increasing awareness and knowledge, reducing instances of illegal real estate practices, and influencing more Filipinos to invest in housing and real estate.

Investing in real estate can be an important tool and wise option to consider for millennials and Gen Z who are looking to build long-term wealth and secure their financial future. A survey showed that millennials who invest tend to start at a younger age (before 21 years old) compared to older generations, but the reality is that they lack confidence in investing due to income challenges, debt, and insufficient knowledge [10]. Another survey affirms that millennials and Gen Z are more likely to have investments in the stock market and cryptocurrency, and only 25% of those investors (23% for millennial investors; 30% for Gen Z investors) own real estate assets, despite prioritizing long-term gains when picking stocks [11]. They are more likely to own Real Estate Investment Trusts (REIT) stocks (36% for millennial investors; 30% for Gen Z investors) than their Gen X and Boomer counterparts. On the flip side, rising property prices, large debts, strict requirements on mortgage applications, and high entry barriers are some of the roadblocks that millennials and Gen Zs alike face when it comes to homeownership, and makes it especially difficult to push through traditional investing options in real estate [12]. Real estate can provide a stable source of passive income through rental properties, as well as the potential for appreciation in value over time.

Several studies have examined the behavioral intention of individuals to invest in real estate and investing in general. A study by Dayaratne and Wijethunga [13] investigated the factors that influence individuals’ behavioral intentions to invest in the Colombo Stock Exchange (CSE) using the theory of planned behavior. The study found that attitudes toward investing and perceived behavioral control are insignificant while the subjective norms significantly impacted individuals’ intentions to invest in stocks. In Jakarta, Maoludyo and Aprianingsih [14] found that perceived financial benefits, perceived product or property value, proximity to anchor developments, and company branding all significantly affected individuals’ intentions to invest in real estate by purchasing a house. In Malaysia, Sulaiman et al. [15] investigated their experts’ views on the feasibility and acceptance of investing in religious properties via Islamic REITs and found that attitudes towards market volatility, risk tolerance, normative principles in Muslim law, and low expected rental income, are among the barriers to investing in Islamic REIT. More recent studies have extended the theory of planned behavior with provisions for sustainability [16,17,18] and have similarly seen significant impacts of attitudes towards a sustainable home and perceived behavioral control on their behavioral intentions to purchase and thus invest in real estate. Overall, the studies suggest that attitudes toward real estate investing, perceived financial benefits, social influence, and perceived risk are all essential factors influencing individuals’ intentions to invest in real estate.

In the Philippines, the media and advertisement play a significant role in shaping public perception and behavior toward real estate investment. Digital technology also has a significant impact on real estate investment. One study by Bjorvatn and Selvik [19] investigated the impact of social media on real estate investment decisions among Iranian investors. The study found that social media platforms such as Instagram and Telegram played a significant role in shaping investors’ perceptions of real estate investment opportunities. The study also highlighted the importance of real estate agents and brokers in using social media to promote their properties and attract potential investors. Another study by Cichy and Gradoń [20] investigated the impact of digital technology on real estate crowdfunding in Poland. The study found that crowdfunding platforms such as CrowdEstate and Housers had enabled more investors to participate in real estate investing, particularly those with limited financial resources. The study also highlighted the importance of regulatory frameworks to ensure transparency and protect investors. Now with an accelerated digital transformation mid- and post-pandemic, social media marketing used in conjunction with automation and disruptive technologies plays a vital role in strengthening the public’s positive attitude towards the properties, increasing foot traffic, and gaining higher revenue [21,22,23,24]. Generally, prior studies have suggested that digital technology has had a significant impact on real estate investment, enabling investors to access information and opportunities more easily, and transforming traditional models of real estate investing.

However, the Philippines is still lagging in adopting digital technology [25]. In terms of the technology landscape in real estate, otherwise known as property technology (PropTech), the country is still in its beginning stages, with a recently established member community called PropTech Consortium of the Philippines bringing about disruptive technology that will change the market [26,27,28]. However, this is beyond the average Filipino, as the general public is primarily aware of specific PropTech in the property search platforms category (e.g., Airbnb). To reach their target markets, most real estate practitioners utilize social media and content creation platforms (i.e., Facebook, Twitter, YouTube, TikTok) to advertise their properties, which makes it difficult to verify the authenticity of the listings posted.

This setup makes it difficult for the average Filipino considering purchasing a new home or looking to rent to verify if the real estate practitioners showing listings are legitimate. For prospective real estate investors, the limited publicly available real-time and historical data that concern the real estate market hinders them from making quick, data-based decisions, improved market research, and more accurate construction planning. In some cases, would-be investors no longer bother to learn due diligence when dealing with potential properties, leading to illegal practices. There is a lack of data on unlawful practitioners in real estate since the Anti-Illegal Real Estate Practices Inter-Agency Task Force at the national, regional, and local levels in the Philippines is still not fully established. As a result, illegal real estate practitioners, commonly referred to as “colorums”, that have been working underground for years have not yet been profiled by the concerned government agencies. Furthermore, the Task Force has yet to establish their database, which should provide relevant data to help them make informed decisions and policy changes. Despite the alarming number of verified reports on these [29], there is still no test case against these colorums.

Thus, the present study aims to determine people’s intention to invest in real estate and how current trends affect the market. This study was conducted to determine the intention of generations Y (millennials) and Z to invest in real estate using an extended theory of planned behavior (ETPB) framework. Particularly in the Philippine real estate industry, this study sought to investigate the direct effects of personal attitude (AT), subjective norm (SN), perceived behavioral control (PBC), risk tolerance (RT), perceived property value (PPV), and aversion from illegal practice (IP) to the investment intentions (II) of millennials and Gen Zs.

In this study, we delve into the critical issue of real estate investment intentions among two distinct generational groups in the Philippines: millennials and Gen Z. Our research question seeks to uncover the primary factors that shape these investment intentions and evaluates the utility of an extended theory of planned behavior framework in elucidating these antecedents.

The framework developed in this study could contribute to investor behavior modeling by validating the significant role of relevant factors on investors’ behavioral intention and its effect on investors’ decisions to invest in commercial and residential real estate. For the concerned member and stakeholder agencies of the National Task Force on Anti-Illegal Real Estate Practice, the results of this study may serve as a supporting study, if not a groundwork, in identifying the warning signs of illegal practices based on behavioral intention. The study could provide insights into improving their systems to protect buyers’ rights better. For the members of the PropTech Consortium of the Philippines, the results of this study may provide the theoretical background necessary to build extensive models to fulfill their visions and objectives for a sustainable, resilient, digitally connected, and inclusive country. The results may also be beneficial for current and prospective investors and real estate professionals in the country, as well as foreign investors and corporations, to be able to strategically assess their transactions with clients, make data-driven managerial decisions for their target market, and protect themselves from illegal transactions. Improvements in the market through governmental support may attract new infrastructure projects, new tenants/buyers, improve customer traction, and ensure customer satisfaction. The benefits extend to the current and future tenants or homeowners since improved market safety will ensure their money is secured and their transactions are free from illegal activities.

Based on preliminary scoping, and government agencies’ priorities, along with time and data constraints, the following scope and delimitations were set: First and foremost, the current research was limited to evaluating latent constructs that influence investors’ intention. It did not include specific measurements for variables such as location, social attributes, mortgage prices, median house/condo values, and other relevant metrics; these are usually assessed in detail for feasibility studies, specifically cost–benefit analyses for development plans. Secondly, this study was focused on commercial (i.e., multi-family properties) and residential (e.g., single type, duplex, apartments, townhouses, accessories, and residential condominiums) real estate that are available in the Philippines [30]. These are the common investments people make in the real estate industry and are the most enticing with their lucrative returns. Thirdly, the study was focused on working millennials and Generation Z in the Philippines. This was not based on the vaguely verified claim stating that millennials will make up 75% of the workforce by 2025, with Gen Zs right on their heels; yet, it was frequently cited by several reputable sources that assumed its legitimacy [31,32,33]. It was instead based on the nature of options they have, their tendencies toward their options, their current economic standing, and how the new generations are reshaping entire markets.

2. Materials and Methods

2.1. Theoretical Framework

The shift to digital space and the overall rapid acceleration to digital transformation vis à vis Industry 4.0 makes it clear that in the modern competitive scenario, different industries seek to use big data with machine learning to mitigate risks and prevent fraud [34,35,36,37]. Government interventions via policy changes and the government’s use of big data and technology play a big role in curbing illegal real estate practices, as was the case in China and Indonesia [38,39]. To this end, the present study aimed to identify the variables that will be able to determine an individual’s intention and decision to invest in real estate through an Extended theory of planned behavior (ETPB).

Developed in the late 1980s, the TPB is a psychological theory that explains human behavior in terms of attitudes, subjective norms, and perceived behavioral control [40,41]. The theory maintains that a person’s attitudes toward a particular behavior, the subjective norms that influence their behavior, and their perceived control over the behavior are the three primary determinants of whether they will engage in that behavior or otherwise. Attitudes refer to an individual’s favorable or unfavorable evaluation of the behavior. Subjective norms refer to an individual’s perception of social pressure to engage in or abstain from a behavior. Perceived behavioral control refers to the extent to which an individual believes he or she can perform the behavior. According to the theory, these three factors interact to determine an individual’s intention to perform a behavior, which then results in the actual behavior [40,41]. To this day, numerous fields, including health and social psychology [42,43,44] and marketing [45,46,47], have made extensive use of the theory to explain and predict human behavior in relation to real estate.

To aid in establishing the framework of the present study, previous research in the field of real estate, specifically in housing under the residential sector, in which TPB was used as a theoretical basis, is summarized in

Table 1. Previous research in the field of commercial and residential real estate.

Author	Influencing Factors on Intention			Content of Intention
	AT	SN	PBC
Li et al. [48]	ü	ü	ü	Exit public rental housing units—China
Mak et al. [49]	ü	ü	n.s.	Promote food waste recycling in the commercial and industrial sector—Hong Kong
Judge et al. [16]	ü	ü	ü	Purchase sustainable housing
Boomsma et al. [50]	ü	ü	ü	Energy saving in inefficient and damp homes—England
Wijayaningtyas and Nainggolan [17]	ü	ü	ü	Purchase green residential buildings—Surabaya and Malang, Indonesia
Tan et al. [51]	ü	ü	ü	Relocate to a smart retirement village
Tang et al. [52]	ü	ü	ü	Participate in neighborhood renewal—China
Shi et al. [53]	ü	ü	ü	Participate in collaborative environmental governance—Yangtze River, China
Islam et al. [18]	ü	n.s.	ü	Buy apartments—Bangladesh
Cheng [54]	ü	ü	ü	Participate in rural resettlement program—China
Hui-Wen Chuah et al. [55]	-	ü	ind.	Repurchase/rebook Airbnb
B. Li et al. [56]	ü	ü	ü	Rent in metropolises—China

Where: AT = Attitude; SN = Subjective Norm; PBC = Perceived Behavioral Control; n.s. = not significant; ind. = indirect effect; - = not measured. In each case, the outcome variable was behavioral intention, except in Tang et al. [52].

. The comparison constructed provides an overview of the state of research, as reflected in the publications found in online research databases and collections such as Business Source Premier updated on EBSCOhost, Philippine E-journals (PEJ), and ProQuest One Academic, and libraries such as the American Society of Civil Engineers (ASCE), IEEE Xplore digital library, and Elsevier B.V.’s ScienceDirect, during the period between February 2023 and March 2023. The keywords used in the literature search were housing, behavioral intention, structural equation model, and TPB.

As seen in Table 1, numerous studies have used the TPB in investigating an individual’s intention to invest in real estate. Mak et al. [49] examined the impact of attitudes, subjective norms, and perceived behavioral control on behavioral intentions to invest in real estate among Hong Kong investors. The results showed that attitudes and subjective norms significantly influenced behavioral intentions, but perceived behavioral control did not. Judge et al. [16] examined the impact of attitudes, subjective norms, and perceived behavioral control on behavioral intentions to invest in real estate. The results showed that attitudes and subjective norms significantly influenced behavioral intentions. Wijayaningtyas and Nainggolan [17] also used the TPB to investigate the factors that influence behavioral intention to invest in residential real estate among Indonesian homebuyers. The results showed that attitudes, subjective norms, and perceived behavioral control significantly influenced behavioral intention.

Prior studies suggest that the TPB is a useful framework for understanding the factors that influence behavioral intention to invest in real estate and can provide insights for real estate marketers and investors. As such, a theoretical framework for the determinants of investment intention of millennials and Gen Z, as shown in Figure 1, was constructed using the three core components—Attitude, Subjective Norm, and Perceived Behavioral Control—with the addition of Risk Tolerance, Perceived Property Value, and Aversion from Illegal Practice (Colorum) as the variables that affect an individual’s intention to invest in real estate.

According to the TPB, attitude toward a behavior, subjective norm, and perceived behavioral control are the primary determinants of a person’s intention to engage in that behavior [40,41]. Succeeding studies that applied TPB in purchase behavior [57], entrepreneurial intentions [58], and stock investment behavior [59], have similar findings on how individuals with a favorable attitude toward purchasing and investing are more likely to view it as socially desirable, which can increase the subjective norm too. That is, when individuals perceive that their social network values investing, they are more likely to invest themselves. In other studies, attitude toward investing has been found to affect both the subjective norm and behavioral control in the context of investments [60,61,62]. Therefore, a positive attitude toward investing can foster an investment-friendly social norm.

In addition, attitudes toward investing have been found to affect perceived behavioral control [41]. Individuals with a positive attitude toward investing are more likely to perceive themselves as capable of making profitable investments, thereby increasing their perceived behavioral control [63,64]. Individuals with a negative investment attitude are more likely to perceive investing as difficult and risky, which can reduce their perceived behavioral control [64]. Given this condition, it was hypothesized that

H1. 

Attitude has a significant and direct effect on investment intention.

H2. 

Attitude has a significant and direct effect on subjective norm.

H3. 

Attitude has a significant and direct effect on perceived behavioral control.

From the perspective of investment, subjective norm has been found to positively influence perceived behavioral control [65]. That is, those who believe that investing is socially desirable may also believe that they possess the skills and resources necessary to invest successfully. The environment as well as social support from family, friends, or coworkers who also invest can boost a person’s confidence in his or her ability to invest successfully, thereby enhancing their perception of behavioral control [58,66,67,68]. Conversely, subjective norm can negatively influence perceived behavioral control. Those who perceive social pressure to invest but lack the financial resources or knowledge to successfully invest may experience a sense of helplessness and diminished perceived behavioral control [69]. Given this condition, it was hypothesized that

H4. 

Subjective norm has a significant and direct effect on investment intention.

H5. 

Subjective norm has a significant and direct effect on perceived behavioral control.

H6. 

Perceived behavioral control has a significant and direct effect on investment intention.

Risk tolerance is a person’s willingness to incur financial risk in pursuit of investment returns. An investor’s risk tolerance then reflects the emotional assessment and comfort with the variability and volatility of the market [70,71]. A higher risk tolerance level indicates a greater willingness to accept investment risk, whereas a lower risk tolerance level indicates a lower willingness to accept risk [72]. According to studies, individuals with a higher risk tolerance are more likely to have a favorable outlook on investing and a greater intent to invest, as individuals with a higher risk tolerance are more willing to accept the inherent risk in exchange for the possibility of higher returns [70,73]. This is consistent with the results of recent studies which indicate that an individual’s risk tolerance is a significant factor in determining their investment intent [74,75,76]. Given this condition, it was hypothesized that

H7. 

Risk tolerance has a significant and direct effect on investment intention.

Perceived property value is an individual’s subjective assessment of a property’s worth. According to studies, perceived property value positively influences investment intent [77,78]. Perceived property value is a significant factor that can influence an individual’s investment intent in the context of real estate investment [79]. These studies observed that when individuals perceive that a property is overvalued or has the potential for future appreciation, they are more likely to have a favorable attitude toward investing in that property and a higher intention to invest. Given this condition, it was hypothesized that

H8. 

Perceived property value has a significant and direct effect on investment intention.

In the Philippines, rebel organizations or anything unlicensed and illegal have been colloquially referred to as colorums (singular colorum) [80]. For the real estate sector, the term colorum refers to an individual or entity that engages in real estate transactions without the proper licenses or permits in an effort to gain higher returns by circumventing zoning ordinances, building restrictions, and other regulations [81]. This unlawful practice that can expose both the buyer and the seller to the risk of fraud, misrepresentation, and other legal issues. According to Yu [82], the presence of colorum operators in the real estate industry can reduce investor confidence and property demand. Additionally, the Texas Real Estate Commission [83] reported in their article that the presence of unlicensed real estate brokers can harm the industry’s reputation and reduce investment activity. The presence of colorums in the real estate industry can have negative effects on investment intentions because unlicensed practitioners may lack the necessary qualifications and expertise to provide accurate and reliable information about the properties they are selling or managing [84]. This can lead to a higher risk of fraud, misrepresentation, and other legal issues that negatively impact investment returns. This was affirmed in the report by the Philippine Daily Inquirer [85], as the presence of unlicensed real estate brokers has caused confusion and mistrust in the industry. This has led to a lack of transparency in transactions and has made it difficult for buyers and sellers to verify the credentials of the brokers, agents, and salespersons they are dealing with. Colorums are practicing underground, so that the concerned government agencies themselves currently only have the capacity to investigate reported incidents and perform undercover operations. It is also important to consider an investor’s option to carry out illegal activities to aid in their intentions and thus behaviors to invest in real estate. As such, the results would not be affected by the public’s knowledge about colorums and illegal RE practices, since it will be based on their personal standards on investments and their capacity to comply with the regulations.

In addition, the presence of colorums can also have a negative impact on risk tolerance, particularly among investors who are aware of the risks associated with unlicensed practitioners. According to Yu [82], the presence of colorum operators in the real estate industry can increase investor risk aversion; that is, investors are more likely to choose low-risk-low-return investments. This is consistent with the findings of Serzo [86], who stated that real estate investors who are aware of the risks associated with colorums may be less willing to take risks. This may result in a decrease in the developer’s brand trust and a drop in property values. Furthermore, should investors and buyers restrict their partnership to only licensed and registered professionals when making deals, it might make it more challenging for unlicensed practitioners to attract clients and gain higher returns. Given these conditions, it was hypothesized that.

H9. 

Aversion from Illegal practice (colorum) has a significant and direct effect on investment intention.

H10. 

Aversion from Illegal practice (colorum) has a significant and direct effect on risk tolerance.

2.2. Participants

This study delimited the participants to persons belonging to Generations Y (millennials) and Z. To achieve this, it was more practical to utilize purposive snowball sampling—a type of non-probability sampling method that works best in applied social research for its approach in “selecting individuals who are most representative of the population”, often selected in conjunction with the individuals’ knowledge and experience with regard to the subject matter [87,88,89]. While the target populations could be further filtered to those who are already active participants in the real estate market in the Philippines and/or to those who reside in urban areas, the expected responses from them suggest the potential for bias to occur. As such, the target population will be the entirety of generations Y and Z, regardless of the level of their knowledge and experience on the market.

Dimock [90], together with Pew Research Center, identified the year 1996 as the cutoff point for Generation Y or more commonly referred to as the millennials, based on where the generation stood on multiple essential formative experiences. Mainly in the United States, such political events were the 9/11 terrorist attack, post-Iraq and Afghanistan wars, and the election of America’s first black president during the 2008 election; economic events, such as the height of an economic recession just as they entered the workforce; and social factors, such as growing up during the internet explosion. The political environment, economic and household dynamics, and other national and global events at the time have shaped the millennial generation’s attitudes and lifestyle mainly because they have a stark recollection and personal engagement with these events, as opposed to the little to no impact they had on Generation Z. Gen Zs, on the other hand, are defined by the rapid progression of the technology landscape that redefined communication and interaction, and subsequently, their lifestyles. The launch of notable mobile phone brands (e.g., Apple, Samsung), ease of web access compounded by Wi-Fi, social media platforms, and rise of content creation platforms are a few things Gen Zs and millennials alike are exposed to.

The millennial generation was henceforth considered as persons born from 1981 to 1996, with ages 27 to 42 in 2023, whereas Gen Zs are persons born from 1997 to 2012, with ages 11 to 26 as of 2023. However, to remain consistent with the objectives of the study, only working individuals were considered. Additionally, the only publicly available data were from the 2020 Census of Population and Housing, where persons aged 15 to 64 years old represent persons who are in their working age up to economically active persons [91]. For this study, purposive sampling was employed to target working millennials and Gen Zs who may or may not have invested in real estate in any way. To calculate the population size, the formula (see Equation (1)) by Yamane [92] was used, wherein n is the sample size, N is the population size, and e is the margin of error.

$$n=\frac{N}{1+N{\left(e\right)}^2}$$

(1)

With a population of 69.4 million Filipinos aged 15 to 64 years [91], the estimated sample size with a 95% accuracy is 400 respondents. This is an appropriate sample size, considering that the recommended sample size ranges from 200 to 500 for PLS-SEM [93,94,95]. The questionnaire was self-administered and distributed using Google Forms for ease of accessibility to most Filipinos.

2.3. Questionnaire

The questionnaire included two (2) parts: the demographics and the extended TPB model on investment intention, which is the theoretical framework formulated in the study. The demographic profile included the gender age, status, area of residence, household size, educational background, employment, and total monthly household income.

Additional characteristics specific to the objectives of the study were the respondents’ (a) familiarity with real estate investment; (b) history of investment/s in real estate; (c) whether the respondent is in search of investment or not; (d) type of property/properties already invested in; (e) type of property/properties willing to invest in; (f) amount of investment budget; (g) location where respondent would like to investment property to be based in; (h) factors prioritized in choosing a property; (i) financing options for investment; and (j) where the respondent usually looks for properties.

For consistency with the Philippine profiling on the seven income groups [96] and the updated poverty threshold of PHP 12,030 per month [97], the income class range was recalculated and summarized in

Table 2. Indicative range of monthly family incomes (for a family size of 5 members), 2021.

Income Class	Definition 1	Indicative Range of Monthly Family Incomes (for a Family Size of 5 Members) 2
Poor	$x<PT$	Less than PHP 12,030 per month
Low Income but Not Poor	$PT<x<2PT$	Between PHP 12,030 to PHP 24,060 per month
Lower Middle Income	$2PT<x<4PT$	Between PHP 24,060 to PHP 48,120 per month
Middle Middle Income	$4PT<x<7PT$	Between PHP 48,120 to PHP 84,210 per month
Upper Middle Income	$7PT<x<12PT$	Between PHP 84,210 to PHP 144,360 per month
Upper Income but Not Rich	$12PT<x<20PT$	Between PHP 144,360 to PHP 240,600 per month
Rich	$x\ge 20PT$	At least PHP 240,600 per month

¹ Adapted from the policy notes on the definition of income levels [98], where x is the per capita income and PT is the official poverty threshold or poverty lines. ² Recalculated using the estimated monthly income for minimum basic food and nonfood needs for a family with five members from the Preliminary 2021 Official Poverty Statistics [97].

. The middle class in the Philippines pertains to those with a monthly indicative family (with a family size of five members) income ranging between PHP 24,060 and PHP 144,360 in 2021 prices.

To remain consistent with the study’s objectives, only working individuals [91] belonging to generations Y (millennials) and Z were considered. As such, for this study, age and generation were loosely categorized in

Table 3. Age groups.

Generation	Definition, Year of Birth	Working Age Group (as of 2023)
Z	1997 to 2012 (Working: 1997 to 2008)	15 to 26
Y, millennials	1981 to 1996	27 to 42

below.

The second part of the questionnaire consists of the indicators based on the proposed framework. This measures the millennials’ and Gen Zs’ intention to invest in real estate. The survey consisted of item questions where all answers were on a five-point Likert scale ranging from “strongly agree” to “strongly disagree”. Seven (7) latent variables were used in the survey which included (1) attitude, (2) subjective norm, (3) perceived behavioral control, (4) risk tolerance, (5) perceived property value, (6) aversion from illegal practice, and (7) investment intention. The summary of measures and constructs is shown in

Table 4. Summary of constructs and measurement Items.

Variable	Item	Measures	References
Attitude	AT1	Investing in the real estate market is rewarding and is a good way to grow my wealth.	[63,106]
	AT2	I believe that investing is an important part of my financial plan and long-term financial goals.	[106,107]
	AT3	I feel confident making my own investment decisions.	[106]
	AT4	I believe that real estate will be a safe investment for me.	[106]
	AT5	I believe investing in real estate will be rewarding to society.	[106,107,108]
Subjective Norm	SN1	People important to me encourage me to invest in real estate.	[106]
	SN2	My friends and family think investing in real estate is not a waste of time and money.	[106]
Subjective Norm	SN3	I feel like my peers who invest in real estate have more rewarding experiences.	[109]
	SN4	I consider what famous financial advisers and other influential people from social media invest in.	[106,109]
	SN5	I feel pressure to invest in companies that have green and socially responsible activities.	[109]
Perceived Behavioral Control	PBC1	I can take the time, effort, and trouble to invest in real estate and monitor it accordingly considering my working conditions.	[106]
	PBC2	I have enough money in my investible funds and/or savings to start investing right now.	[107]
	PBC3	I have access to and am eligible to access other methods to finance my investments.	[107]
	PBC4	I have complete knowledge of various ways I can invest in real estate and the skills to do so effectively.	[99]
	PBC5	I have access to reliable investment advice and other resources to help me assess my options.	[109]
	PBC6	I have high confidence to invest when the platform, project, and project developers have earned my trust and support.	[67,109]
Risk Tolerance	RT1	I would consider taking higher risks in investing based on my level of education and the degree of knowledge I have gained in the real estate market.	[70,73]
	RT2	I will surely invest a certain amount of money in real estate should I unexpectedly receive some easy money.	[73]
	RT3	I would prefer to invest in REITs, REIGs, and crowdfunding than keep my money in a bank account.	[73]
	RT4	I consider calculated risk in investment properties as an opportunity.	[70,73]
	RT5	If I happen to lose some money in my investments, I will not mind because I can recuperate with my income.	[70,73]
Perceived Property Value	PPV1	I am confident investing in properties that have or are near anchor developments (e.g., airports, skyways, LRT/MRT stations, malls, major schools/universities, etc.).	[100]
	PPV2	I feel delighted in investing in properties where the gains are high.	[100]
	PPV3	I believe I should invest in properties that cater to highly profitable target markets (e.g., university housing for students, and areas near workplaces like BGC for middle- to high-income renters).	[8,100]
	PPV4	I feel trust and confidence in investing in sustainable housing because they are long-lasting and have a low environmental impact.	[16,101]
	PPV5	The property’s quality is high when it is built sustainably, uses eco-friendly materials, and features energy efficiency.	[16]
Aversion from Illegal practice (Colorum)	IP1	I feel confident investing if the property I’m interested in has all the required government-issued documents.	[102]
	IP2	My trust in the developers, brokerage, and other related entities, is high when they communicate with me in a fair, fully transparent, and consistent manner.	[103]
	IP3	I am confident that my investment will be worthwhile if the infrastructure conforms to zoning ordinances and the building code.	[102]
	IP4	I don’t want to associate myself with illegal real estate practitioners because it will give me and my investment portfolio a false image.	[104]
	IP5	I want to put my hard-earned money into investment properties that are of good quality and legitimate/legal.	[104]
Investment Intention	II1	I am willing to invest in physical/traditional real estate in the future.	[106,109]
	II2	I am willing to invest in non-physical real estate options in the future.	[106,109]
	II3	I intend to invest in projects and/or companies that are sustainable and socially responsible.	[105,109]
	II4	I predict that our society will predominantly support housing projects that are sustainable and affordable.	[105,109]
	II5	I intend to encourage others to invest in sustainable housing projects.	[105,109]

. The items for the constructs were adopted from and/or were based on the results of existing reports and studies [8,16,63,67,70,73,99,100,101,102,103,104,105,106,107,108,109].

For clarity, the operational definition for each construct was provided in the survey. The investment options considered for this study, and those that are available in the Philippines, fall into one of two categories: Physical and Non-Physical. Physical or Traditional come in the form of (a) Residential Real Estate such as the selling of single-family homes, condos, cooperatives, duplexes, townhouses, multi-family residences, and vacation homes; (b) Commercial Real Estate, which covers any property that is rented or leased to earn income, but for this study, will only cover multi-family rental properties; (c) House Flipping, where investors will purchase properties (e.g., distressed, bank foreclosures) and renovate to sell or rent; and (d) Raw Land, where underdeveloped properties, farms or croplands, orchards, recreational lands, and lands for mineral production are purchased, possibly enhanced, and sold or rented. With the onset of digital space, Non-Physical investment options have emerged in the form of (a) Real Estate Investment Groups (REIGs), (b) Real Estate Investment Trusts (REITs), and (c) Online Real Estate Platforms or Real Estate Crowdfunding, all of which make it possible for people to invest in real estate even without a large investible fund, more secure investments, and high liquidity among others, but with no real sense of ownership or control over the properties.

2.4. Structural Equation Modeling

The data collected from the survey were analyzed using structural equation modeling and multivariate analysis. This study employed a variance-based partial least squares SEM (PLS-SEM) with maximum likelihood estimation. PLS-SEM is a tool for examining the relationships between abstract concepts [93], which deals with complex constructs at higher levels of abstraction and generates higher construct reliability and validity, making it ideal for prediction [110] and applicable to this study. Its primary objective is to maximize the variance explained in the dependent constructs, but data quality is also evaluated based on the characteristics of the measurement model. According to Ouellette and Wood [111], PLS-SEM differs from earlier modeling approaches in that it considers both direct and indirect effects on hypothesized causal links and is increasingly utilized in scientific research and studies. Moreover, PLS-SEM is the method of choice for theory development and prediction, whereas CB-SEM is superior for testing and validating existing theories [112].

The real estate market has a high degree of complexity on account of the many factors that are difficult to measure and predict, as well as a certain degree of uncertainty brought upon by unforeseen events and phenomena [113,114,115]. In response to this, frameworks and models were developed to explain influencing factors, market drivers, investor behavior, budget allocation, valuation, pricing, and the like, and were published to provide useful insights for a better understanding and explanation of patterns and trends in the market. The study of French [116], for instance, analyzed behavioral aspects (i.e., attitude, perception of risk) of pension fund managers in the UK through the lens of decision theory. Simons [117] investigated in his qualitative research the effect of the environment on property values, specifically on contaminated land and/or properties. Using multiple regression analysis, Lawson [118] found that price theory can be used as a working theory for real estate valuation for an investor’s risk assessment. With the rise of disruptive technologies that aid in real estate, more studies utilizing modern statistical and analytical methods were published to bridge gaps. For example, to assess and evaluate the state and condition of residential properties in urban areas, Renigier-Biłozor et al. [119] developed a scoring system and verified it using the Delphi method Estimate-Talk-Estimate (ETE). Pérez-Rave et al. [120], on the other hand, utilized machine learning to predict real estate prices in Colombia. What these prior studies highlight are the importance of information and the method in analyzing said information. As everyday transactions quickly transition to the digital world and industries become more reliant on big data to improve their processes, there is an increasing need for the real estate market to catch up.

In the empirical study of Akter et al. [121], partial least squares structural equation modeling (PLS-SEM) was proven to be a suitable tool for analyzing big data. The application of PLS-SEM in the real estate industry has been a subject of interest in the recent literature. In Nigeria, the theory of planned behavior was used to explain the intention to finance house purchase with a mortgage [122]. In China, the soft budget constraint theory was used to determine the driving forces of local governments in their land financing strategy [123]. In Bangladesh, the impact of managers’, investors’, and firms’ perceptions of the issues of corporate governance and specific determinants (e.g., profitability, growth opportunity, business risks) on their leverage structure decisions was investigated [124]. In India, the theory of planned behavior was used to investigate the consumers’ purchasing intentions on residential properties [45]. As of writing, relevant studies on the application of PLS-SEM in the Philippine real estate industry has a limited coverage of topics, such as examining factors affecting managers’ choices of accounting method using the positive accounting theory [125], and the impact of property management services on residential property value [126]. In line with the goals of the government with its digitalization and use of big data, this study tested the reliability of PLS-SEM in analyzing big data to develop a framework on real estate investment, befitting from the Philippine context.

3. Results

3.1. Demographic Profile

A total of 400 respondents participated in the online survey conducted from May 2023 to July 2023. As seen in the breakdown of the respondents presented in

Table 5. Summary statistics of demographic profile.

Respondents’ Profile	Category	N	%
Generation	Generation Y (millennials)	213	53.31
	Generation Z	187	46.69
Gender	Male	192	47.93
	Female	208	52.07
Status	Single	273	68.18
	Married	114	28.51
	Separated	13	3.31
Area of Residence	Region I—Ilocos Region	73	18.18
	Region II—Cagayan Valley	2	0.41
	Region III—Central Luzon	53	13.22
	Region IV-A—Calabarzon	43	10.74
	MIMAROPA Region	2	0.41
	Region V—Bicol Region	2	0.41
	Region VI—Western Visayas	2	0.41
	Region VII—Central Visayas	5	1.24
	Region VIII—Eastern Visayas	3	0.83
	Region IX—Zamboanga Peninsula	2	0.41
	Region X—Northern Mindanao	10	2.48
	Region XI—Davao Region	10	2.48
	Region XII—SOCCSKSARGEN	3	0.83
	Region XIII—Caraga	2	0.41
	NCR—National Capital Region	187	46.69
	CAR—Cordillera Administrative Region	3	0.83
	BARMM—Bangsamoro Autonomous Region in Muslim Mindanao	-	-
Educational Background	Attended Grade School	-	-
	Attended High School	12	2.89
	Attended Senior High School	88	21.90
	Attended some Technical Vocational Course Subjects	18	4.55
	Finished Vocational degree	18	4.55
	Credited some units for Bachelor’s degree	28	7.02
	Finished Bachelor’s degree	217	54.13
	Credited some units for Master’s degree	8	2.07
	Finished Master’s degree	7	1.65
	Credited some units for Doctorate	-	-
	Finished Doctorate	5	1.24
Degree Major	Finance	63	15.70
	Non-Finance	223	55.79
	N/A	114	28.51
Employment	Unemployed	43	10.74
	Student	73	18.18
	Employed	223	55.79
	Self-employed/Business Owner	61	15.29
Income Class	Poor	38	9.50
	Low Income but Not Poor	78	19.42
	Lower Middle Income	124	30.99
	Middle Middle Income	81	20.25
	Upper Middle Income	46	11.57
	Upper Income but Not Rich	18	4.55
	Rich	15	3.72
Investment Budget	PHP 20,000 to PHP 100,000	213	53.72
	PHP 500,000 to PHP 1,000,000	187	30.17
	Above PHP 1,000,000	192	16.12

, the majority of the respondents are millennials (Generation Y), covering about 53.31%. Among the millennials, 21.71% were 27- to 29-year-olds, 48.94% were 30- to 39-year-olds, and 29.46% were 40- to 42-year-olds. The remaining 46.69% of the total respondents were made up of Generation Z, who were 15- to 19-year-olds and 20- to 26-year-olds, with a percentage of 12.39% and 87.61%, respectively. It was observed that 53% of the respondents were female while 47% were male.

The survey covered all regions in the Philippines except BARMM. Most of the respondents were living in NCR (46.69%), followed by the Ilocos Region (18.18%), Central Luzon (13.22%), and Calabarzon (10.74%). The respondents were mostly of the middle-income class, with the Lower Middle Income (between PHP 24,060 to PHP 48,120 per month) accounting for 30.99%, Middle Middle Income (between PHP 48,120 to PHP 84,210 per month) accounting for 20.25%, and Upper Middle Income (between PHP 84,210 to PHP 144,360 per month) accounting for 11.57%. A total of 19.42% of the respondents were reportedly of the Low Income but Not Poor class (between PHP 12,030 to PHP 24,060 per month).

Additionally, most of the respondents (311 or 77.79%) had prior knowledge or were familiar with real estate investment, 167 (53.72%) of which had experience investing in the real estate market. These investors mostly invested in Residential Real Estate, followed by Raw Land and Commercial Real Estate. It was observed that 85.54% of the respondents were interested in investing in their area of residence, among others. The National Capital Region, Ilocos Region, Central Luzon, and Calabarzon were observed to be the most desired locations for investment properties.

Pag-IBIG loans and bank loans were reportedly the main financing options for investing among the respondents. The majority of those who responded had an investment budget of PHP 20,000 to PHP 100,000, representing 53.72% of the respondents, followed by PHP 500,000 to PHP 1,000,000 and above PHP 1,000,000, with a percentage of 30.17% and 16.12%, respectively.

3.2. Results of SEM

Figure 2 illustrates the initial model that served as a guide to demonstrate the relationship between factors affecting the investment intention of millennials and Gen Z. The model is composed of seven latent variables and 36 indicators. The observed values shown for each indicator were measured using the items included in the questionnaire.

Table 6. Reliability and convergent validity result.

Variable	Items	Mean	S.D.	FL (≥0.7)	α (≥0.7)	CR (≥0.7)	AVE (≥0.5)
Attitude (AT)	AT1	1.86	0.92	0.842	0.880	0.886	0.678
	AT2	1.82	0.94	0.846
	AT3	2.29	1.04	0.734
	AT4	2.10	0.95	0.868
	AT5	2.02	0.95	0.829
Subjective Norm (SN)	SN1	1.97	0.98	0.830	0.867	0.871	0.655
	SN2	2.12	0.93	0.785
	SN3	2.12	0.89	0.879
	SN4	2.32	0.98	0.781
	SN5	2.22	1.08	0.765
Perceived Behavioral Control (PBC)	PBC1	2.17	0.96	0.815	0.933	0.946	0.750
	PBC2	2.59	1.05	0.782
	PBC3	2.38	0.99	0.876
	PBC4	2.50	1.10	0.881
	PBC5	2.38	1.05	0.929
	PBC6	2.28	1.02	0.906
Risk Tolerance (RT)	RT1	2.25	0.91	0.838	0.849	0.873	0.620
	RT2	2.27	0.89	0.732
	RT3	2.38	0.87	0.768
	RT4	2.20	0.92	0.808
	RT5	2.62	1.16	0.788
Perceived Property Value (PPV)	PPV1	1.92	0.87	0.837	0.902	0.910	0.719
	PPV2	1.87	0.89	0.835
	PPV3	1.91	0.94	0.838
	PPV4	1.95	0.88	0.913
	PPV5	1.90	0.94	0.825
Aversion from Illegal Practice (IP)	IP1	1.86	0.99	0.935	0.947	0.951	0.825
	IP2	1.98	0.91	0.907
	IP3	1.94	0.87	0.893
	IP4	1.88	1.01	0.912
	IP5	1.86	1.00	0.893
Investment Intention (II)	II1	1.93	0.95	0.788	0.876	0.882	0.671
	II2	2.19	0.95	0.740
	II3	2.02	0.91	0.828
	II4	2.11	0.88	0.856
	II5	2.00	0.89	0.875

displays the reliability and validity values of the observed data for the final model. Factor loadings (FL) express the relationship of the item to the construct, where values higher than or equal to 0.7 indicate sufficient variance from the construct. Since the values have large positive loadings, none of the items were omitted in the testing. Values evaluated using Cronbach’s alpha (α) indicate the good internal consistency of the items. The values for the composite reliability (CR) all exceed 0.7, which is a good indication of the internal consistency and reliability of the constructs. The average variance extracted (AVE) measures the convergent validity or the communality of the constructs, and the calculated values all reveal that the indicators are representative of the underlying construct. Overall, the results demonstrate adequate internal consistency, reliability, and validity [127], suggesting that all the constructs in the model are reliable and valid.

Table 7. Discriminant validity: Fornell–Larcker criterion.

	AT	IP	II	PBC	PPV	RT	SN
AT	0.823
IP	0.645	0.908
II	0.656	0.717	0.819
PBC	0.643	0.445	0.663	0.866
PPV	0.618	0.801	0.735	0.472	0.848
RT	0.665	0.550	0.730	0.743	0.613	0.788
SN	0.697	0.624	0.723	0.696	0.740	0.670	0.809

and

Table 8. Discriminant validity: Heterotrait/Monotrait Ratio.

	AT	IP	II	PBC	PPV	RT	SN
AT
IP	0.702
II	0.737	0.783
PBC	0.688	0.446	0.706
PPV	0.685	0.759	0.816	0.490
RT	0.757	0.570	0.826	0.838	0.662
SN	0.787	0.686	0.821	0.753	0.833	0.758

display the discriminant validity values using the Fornell–Larcker criterion and the Heterotrait/Monotrait correlation ratio, respectively, following the approach developed by Henseler et al. [94] to analyze the relationships between the latent constructs in variance-based SEM. The method posits that the square root of a construct’s AVE, shown in the diagonal of Table 7, should be greater than its correlations with the other constructs. That is, the values in the diagonal should be greater than the rest of the values in their respective row and column. The threshold for Heterotrait/Monotrait ratios must be less than 1 to express discriminant validity, and lower than 0.85 to assess whether closely related latent variables are overlapping or otherwise [128]. Overall, there are no issues of reliability, convergent validity, and discriminant validity. The values all fall within the intended range, confirming that the constructs in the model are indeed valid and reliable.

3.3. Model Fit Analysis

A model fit analysis was performed to assess the reliability of the proposed model. The Standardized Root-Mean-Squared Residual (SRMR), chi-square, and Normal Fit Index (NFI) were the parameters used in this study. The model fitness indices reported in

Table 9. Model Fit.

Model Fit for SEM	Parameter Estimates	Minimum Cut-Off	Recommended by
SRMR	0.061	<0.08	[129]
(Adjusted) Chi-square/dF	4.23	<5.0	[130]
Normal Fit Index (NFI)	0.921	>0.90	[131]

are all within the acceptable range and further confirm that the proposed model is valid.

3.4. Results of Final SEM

The proposed hypotheses were tested with the partial least squares structural equation modeling (PLS-SEM) using the Smart PLS application, the results of which are presented in

Table 10. Hypothesis test.

No.	Relationship	Beta Coefficient	p-Value	Result	Significance (p-Value < 0.05)	Hypothesis
1	AT → II	0.009	0.937	Positive	Not Significant	Reject
2	AT → SN	0.697	<0.001	Positive	Significant	Accept
3	AT → PBC	0.306	0.027	Positive	Significant	Accept
4	SN → II	0.119	0.281	Positive	Not Significant	Reject
5	SN → PBC	0.483	<0.001	Positive	Significant	Accept
6	PBC → II	0.189	0.187	Positive	Not Significant	Reject
7	RT → II	0.447	<0.001	Positive	Significant	Accept
8	PPV → II	0.691	<0.001	Positive	Significant	Accept
9	IP → II	0.276	0.019	Positive	Significant	Accept
10	IP → RT	0.550	<0.001	Positive	Significant	Accept

. It was observed that risk tolerance (RT) (β = 0.447; p < 0.001), perceived property value (PPV) (β = 0.691; p < 0.001), and aversion from illegal practice (IP) (β = 0.267; p = 0.019) all had a significant and positive influence on the respondents’ investment intentions (II). In contrast, attitude (AT) (β = 0.009; p = 0.937), subjective norm (SN) (β = 0.447; p < 0.001), and perceived behavioral control (PBC) (β = 0.189; p = 0.187) were shown to have no significant effect on the investment intentions, and their respective hypotheses were, therefore, rejected. On the other hand, it was observed that attitude (AT) had a positive association with subjective norm (SN) (β = 0.697; p < 0.001) and perceived behavioral control (PBC) (β = 0.306; p = 0.306). A positive association was also found between subjective norm (SN) and perceived behavioral control (PBC) (β = 0.483; p < 0.001), and between aversion from illegal practice (IP) and risk tolerance (RT) (β = 0.550; p < 001).

The final SEM model evaluated using the beta coefficient and R² values is illustrated in Figure 3. The model assigned 30.3% of the variation to risk tolerance, 48.6% to the subjective norm, 53.3% to perceived behavioral control, and 72.4% to investment intention. Provided that the R² values are above the 20% threshold [94,112], the model is proven to have an adequate and acceptable variation to explain the aforementioned constructs.

4. Discussion

Many low-income and middle-income families in the Philippines reportedly face significant barriers to homeownership, compounded by interrelated social and economic issues the country has been facing for decades. This in turn has forced some to become illegal settlers and may also have resulted in illegal real estate practices to afford decent housing. It stands to reason that these housing issues are also dealt with using multiple solutions. One of the approaches is investing in the real property market and other main segments in the real estate sector. In the wake of the COVID-19 pandemic, the Philippines experienced rapid growth in e-commerce and digital media. In particular, Philippine Real Estate is one of the many industries that have been thriving in the digital economy since 2021. Following the enactment of the Corporate Recovery and Tax Incentives for Enterprises (CREATE) Act [132], the country earned the highest greenfield investment inflow growth rates in Southeast Asia for 2022 with a 53% increase [133]. The total value of approved foreign direct investments (FDI) for the Real Estate activities industry amounted to PHP 57.15 billion in 2022 [134], with a 23.63% share second only to the Information and Communication (ICT) industry, with its 47.30% share [135]. The RE market demand that caters to the foreign investors from Singapore, Japan, Netherlands, and United Kingdom—countries where the majority of the 2021 to 2022 investors are from [135]—and other countries for that matter, is expected to increase from 2023. Particularly, the increasing demand for condominium units and rental properties in strategic locations near commercial establishments, such as offices, industrial units, and retail, are what local investors would be more inclined to meet. Just the same, there is a need to digitize and improve the transactions and operations for RE activities.

The government has made steps in accelerating its digital transformation to provide better service but also to strengthen the country’s industries. However, while the digital divide in the Philippine RE market has been bridged by new disruptive technologies, illegal real estate practices remain prevalent and instead have increased with the benefits accompanied by the internet and social media platforms. The country’s digital transformation highlights the use of big data analytics to mitigate risks and prevent fraud. Akter et al. [121] found partial least squares structural equation modeling (PLS-SEM) to be a suitable tool for analyzing big data. To take the first step in confirming its validity and usability in the Philippine context, this paper examined the effects of attitude, subjective norm, perceived behavioral control, risk tolerance, perceived property value, aversion from illegal practice, and investment intentions of millennials and Gen Zs in the real estate market using an extended theory of planned behavior (ETPB) framework.

The data analyzed in the study were gathered from around the middle of Fiscal Q2 (May and June 2023) to the start of Fiscal Q3 (July 2023). During this time, the country has reportedly seen slower economic growth with a 5.6% increase, possibly attributed to the dampened household consumption as the younger labor force consisting of millennials and Gen Zs can barely cope with the burden of inflation and are reluctant to spend more outside their necessities [136,137,138]. Furthermore, the Philippines maintained its lower-middle-income country status as the World Bank [139] classified it, although it is still on its way to upper-middle-income status based on the previously reported accelerated increase in its gross domestic product. The respondents of this study appear to represent the large and growing middle class as highlighted in the recent statistics, comprising about 62.75% of the total. It was also observed that 30.58% have dependents (children), 29.93% may be the dependents themselves (student or unemployed), and 66.53% possibly contribute to paying their own and/or their family’s monthly expenses (represented by respondents who either live by themselves, with co-tenants, with an extended family, or their own family unit). These economic conditions allude further as to why a majority of the respondents (53.72%) reportedly only have an investment budget of PHP 20,000 to PHP 100,000 in this period.

The demographic profile in Table 5 presumes that the survey mainly targeted women aged 27 to 42 years old (millennials). They, however, only cover 31.4% (216) of the respondents, followed by 26.03% (104) comprised of Gen Z men, 21.9% (88) comprised of millennial men, then 20.66% (83) comprised of Gen Z women. This suggests that the demographic composition appears to be varied enough based on gender and age, considering that the participants were gathered using purposive snowball sampling. Another limitation expressed in the results is that each region is not sufficiently represented according to their respective current population, as 46.69% reside in the National Capital Region (NCR). It was also observed that 85.54% of the respondents were interested in investing in their area of residence. Considering that the respondents mostly reside in NCR, Ilocos Region, Central Luzon, and Calabarzon, it follows that these regions are also the top desired locations for investment properties. This attests to the notion of “home bias”, wherein investors prefer to purchase properties locally or near their area of residence, which has also been observed in the recent literature [140]. It follows that should the demographic composition of the respondents be equally represented, they would still prefer to invest in properties in areas they are familiar to, given the decentralized nature of Philippine RE and the high barrier to entry in regional RE markets. Considering the demand for rental properties near commercial establishments, the results were consistent, with 47.7% of the total share of approved foreign and Filipino investments scattered in various regions in the country [135]. This report also showed that the top regions with the largest shares for Q4 2022 were Calabarzon (20.5%), Ilocos Region (18.8%), Central Luzon (8.6%), and NCR (1.4%) [135].

Considering that the model itself has no issues with internal consistency, convergent validity, discriminant validity, and model fit, it is probable that the hypotheses were disproven because of the model’s data. Based on the review of recent PLS-SEM studies by Guenther et al. [141], the estimated values should be considered as “proxies [that] resemble but do not perfectly represent and measure” the latent constructs since they are based on the available data. Furthermore, the TPB model explains the behavioral intentions of the target respondents in different situations [13], so it is possible that there was another layer unaccounted for in the model that also impacted the population being studied. Since they were experiencing inflation during the data-gathering phase, the investment activities of middle-income millennial and Gen Z Filipinos were not guided by their attitude toward investing in real estate. The subjective norm for these middle-income current and potential investors was also not supportive of investing in real estate in times of inflation, nor were their investing intentions and decision-making based on their self-efficacy to do so.

Previous research in the field of real estate that used TPB also arrived with the same result of insignificant TPB domains, such as the case in Hong Kong [49], Sri Lanka [13], and Bangladesh [18]. Although said studies only found one or two domains to be insignificant, the result of those and of this current study remain consistent with the idea that the real estate market often defies theory. That is, the socio-economic–political environment an investor is subjected to, unpredictable market phenomena experienced, behavioral and psychological biases, and many other factors that influence it, all make it difficult to apply a simple and/or singular theory to explain the investors’ behaviors [113,114,142,143,144,145,146]. These further attest the importance of the social, cultural, economic, and political context to which this theoretical model is applied to, since this could still prove to be useful in understanding and analyzing the real estate market during its different cycles [115].

In consideration of the Filipino culture, the findings could be explained by the high proportion of extended-family households in the country, which was also observed in this study’s respondents [147]; while there is no definitive proof of a negative household formation, it is one indicator that suggests this phenomenon. In other words, their subjective norm would not be supportive of their investment in residential properties, as reflected by their cultural preference for living with relatives. Economic constraints, such as low income or their civil status (i.e., single), may also discourage or delay household formation among young adults. On the flip side, their capacity to afford invest in real estate, benchmarked with their income, may simply not matter to them provided with the option for housing loans like Pag-IBIG.

While the latent construct attitude (AT) was proven to not influence the respondents’ investment intention (II), it did have the most decisive, significant, direct, and positive effect on the subjective norm (SN) (β = 0.697; p < 0.001), thereby accepting H2. This means that when a person holds a positive attitude toward real estate investment and perceives social support and approval for the behavior, their intention to invest in real estate is likely to be higher. It follows then that when an individual has a great evaluation of the potential returns in real estate (AT1), regards real estate investment as important to their own financial goals (AT2), has confidence or overconfidence in their investment decisions (AT3), positively regards real estate investments as safe (AT4), and believes investing in real estate brings about positive societal impact (AT5), they are more likely to foster an investment-friendly social norm. That is, they are more likely to accept positive feedback from their friends, family, peers, and positive information from the online content they consume. This is consistent with studies by Shah Alam and Mohamed Sayuti [57], Sait Dinc and Budic [58], and Lai [59], who all found positive and significant associations between attitude and subjective norm in different investment applications of the TPB. Thus, real estate professionals, financial advisers, and marketers may customize their methods to attract or assist prospective investors by having an understanding of how attitude and subjective norm interact to influence people’s investing choices.

Both attitude (AT) and subjective norm (SN) are found to have a significant, positive, and direct effect on the respondents’ perceived behavioral control (PBC), thereby accepting H3 and H5. The subjective norm (β = 0.483; p < 0.001) was proven to have a more significant effect on the respondents’ self-efficacy in investing in real estate. This means that when an individual perceives positive subjective norms, such as social support and encouragement from others to invest in real estate, it can positively impact their perceived behavioral control. Supportive subjective norms can boost their confidence in their ability to invest and overcome potential obstacles, thus increasing their perceived control over the behavior. The indicators proved that millennials and Gen Zs who receive positive encouragement (SN1), opinions (SN2), and testimonies (SN3) from their friends, family, and peers; consume positive influence from online content creators (SN4); and receive social pressure to have their investment behaviors impact society and the environment positively (SN5) allow them to have a better disposition of their capacity to invest in real estate. The results are similar to the statements from the previous literature [58,63,64,66,67,68,69]. It follows that when the individual has a positive view of real estate and the subjective norm is supportive of the investing intention and behavior, it is more likely that the individual will be willing to take the time and effort required to invest in real estate (PBC1), has the financial capacity to invest (PBC2), has access to multiple funding options (PBC3), is knowledgeable in real estate investing (PBC4), has access to reliable sources of advice (PBC5), and has confidence or overconfidence to push through with their investment plans (PBC6).

Out of the seven accepted hypotheses, three of those have a positive and direct effect on the intention of millennials and Gen Zs to invest in real estate. These include risk tolerance (RT), perceived property value (PPV), and aversion from illegal practice (IP), thereby accepting H7, H8, and H9, respectively. As it happens, these three latent constructs extended the main theory of planned behavior (TPB) model.

Chmielewska et al. [148] analyzed the investment transactions of residential real estate in five cities in Poland pre- and mid-pandemic and revealed a new preference among investors, which is to purchase properties located away from city centers and possibly anchor developments with the desire for larger space and better views. A study by Hossain et al. [149] found that in some property valuers in the United Kingdom, sustainability in commercial real estate is more important for unit owners or occupants than for investors—this could not be said, however, of the entire population of property valuers in the United Kingdom. Contrary to these studies, post-pandemic investors in the Philippines significantly consider traditional notions in their property valuation (β = 0.691; p < 0.001) for their intention to invest in real estate. In line with this, it could be implied that millennial and Gen Z investors with positive subjective assessments of a property’s location and proximity to anchor developments (PPV1), its expected gains (PPV2), the profitability of its target market (PPV3), its sustainability (PPV4), and its quality (PPV), are more likely to consider investing in that property. This implies that real estate market dynamics, investor sentiment, and external influences can all shape the perceived value of properties and influence investment decisions. The results of this study contribute to the growing literature which proved the significant influence of the perceived property value to the investment intent [77,78,79].

Cupák et al. [150] found in their investigation of US household 2019 Consumer Finances wealth microdata that investors’ financial literacy and confidence level have a significant and positive relationship with their market participation in risky assets, and that a low confidence level could deter investment behavior despite having an evidently higher financial literacy. The study of Cardak and Martin [151], which investigated the determinants of risk tolerance based on real stock market returns, accounting for economic and financial crises, found that millennials are more willing to take financial risks than their Gen X (Silent Generation) counterparts, and that the financial and investment decisions of Australian households are influenced by long-term benefits. In support of these, this study also found risk tolerance (RT) (β = 0.447; p < 0.001) to have a significant, positive, and direct effect on the investment intention (II) of millennials and Gen Z in the real estate market. This implies that individuals who are comfortable in taking higher risks (RT1) are willing to invest no matter the amount of investible money they have, (RT2) are willing to accept higher risks in exchange for higher returns compared to slow returns in high-yield savings accounts or even no returns in traditional banks, (RT3) positively views risks as opportunities (RT4) and are more comfortable incurring losses in their investments, (RT5) and are more likely to invest in real estate regardless of the economic conditions. This remained consistent with previous studies about investing in general [152,153,154], as well as similar studies that proved the significant effect of risk tolerance to their investment intent [74,75,76].

Finally, although not as effective as the risk tolerance and perceived property value, aversion from illegal practice or colorums (IP) (β = 0.276; p < 0.001) was also proven to have a significant, positive, and direct effect on investment invention (II). Additionally, IP (β = 0.550; p < 0.001) was proven to have a significant, positive, and direct effect on the investors’ risk tolerance (RT), thereby accepting H10. The results imply that when an investor is strict on the legality of real estate practices on multiple levels such as the transparency and completeness of legal documents (IP1), consistency of partner-real estate practitioner’s behavior (IP2), conformity of the property to zoning ordinances and building code (IP3), the effect it has on their personal investment portfolio (IP4), and choosing only to profit from properties that are of good quality and are legitimate/legal (IP5), they have a higher risk tolerance in their investment intentions. The findings of Baguisi and Lin [155], in their study of Real Estate Foreign Direct Investments (FDI) in the Philippines, were a falsification of the older literature [156,157] and were consistent with the more recent literature, which attests how corruption demonstrated by low transparency deters real estate FDI inflows. Likewise, this current study contributes to the growing literature which proves the adverse effects of illegal real estate practices in the real estate market. According to Chen et al. [158], potential investors may be significantly discouraged from moving forward with their investment plans if they believe that there are dishonest or illegal activities are taking place in the real estate industry. As Francis and Armstrong [159] suggest, when considering real estate investments, investors must place a high value on transparency, honesty, and legal compliance. It is also essential for authorities and industry stakeholders to combat illegal practices rigorously and promote ethical behavior in the real estate sector [160].

4.1. Theoretical Implications

In pursuit of the Philippine government’s digital transformation, it is imperative to study the preferences and behaviors of your citizens for good service, as well as utilize big data and analytics to set benchmarks and aid in decision-making [161]. Partial Least Squares Structural Equation Modelling (PLS-SEM) was proven to be one of the many suitable tools for analyzing big data [121], and this study is one of the first to validate this claim and assess the reliability and usability of such a tool in the Philippine context, specifically in the real estate market. To this end, the present study applied the theory of planned behavior (TPB) to determine the factors that affect investment intentions—extending the theoretical model with relevant constructs that appeared in the previous literature, namely, risk tolerance, perceived property value, and aversion from illegal practice—and develop a theoretical framework of real estate investment in the Philippine context. Unlike previous studies that developed models for investing in general, this model was able to capture caveats in the Philippine context, and the results are consistent with recent events relevant to real estate. Furthermore, it considers the intentions of a consumer group, knowing that they have the option to perform treacherous actions as a means to their end. The results identified that the model is reliable and valid and, thus, sufficient to explain the investment intentions.

The hypothesis tests of the study debunked the TPB—with its well-known and established outcome that the attitude, subjective norm, and perceived behavioral control underlie the intention to perform a behavior [40,41]—at least in the context of the investment intentions of millennials and Gen Zs in the Philippine real estate market from the middle of Quarter 2 and the beginning of Quarter 3 in the fiscal year 2023. The results instead only showed the perceived property value, risk tolerance, and aversion from illegal practice as significant factors to investment intention and possibly investment behavior. This negates the recent literature that found investment decisions consistent with behavioral psychology theory [162], though it ratified studies that found rationality in the decision-making and valuation of real estate investors [116,163,164,165]. Thus, the findings of this study showcase rationality and practicality in their investment intentions and decision making.

In addition, the investment intentions are consistent with the cyclical nature of the real estate market, in which there are fewer property developments due to higher costs of construction, leading to lower inventory that keeps demand high, which then incentivizes sellers to increase house prices [166,167]. The significant effect of the perceived property value is consistent with the theory of investment into a build and human environment, in that a preference to invest in anchor developments (e.g., educational institutions, recreational centers, etc.) reflects the human need for better communities, cleaner environments, and a better standard of living [168]. The openness of the respondents in taking higher and calculated risks implies that the principle of substitution [169] and principles of regression and progression [170] could be relevant in their decision making. Targeting highly profitable markets, such as office workers and students, would require them to choose a property nearer to their market’s respective destinations (i.e., offices, universities) and by the principle of progression, the value of the land and property are higher the closer it is to city centers. Considering that a tenant, by the principle of substitution, would opt for a less expensive rental unit near their office or school, the investors would cut costs at the expense of better-quality facilities. This has already been observed in practice with the presence of functionally obsolete and privately-owned dormitories, boarding houses, condominiums, and apartments in urban areas, evidenced by the poor living conditions, lack of sanitation, small spaces, inadequate ventilation, and bland architecture [171,172,173]. Overall, the findings imply that such basic economic principles and theories of investment in property and real estate are applicable in the Philippine context.

This present study developed a theoretical framework using TPB that was extended with factors such as the investor’s risk tolerance, perceived property value, and aversion from illegal practice. The model is able to explain the behavioral intention of a certain demographic within a defined period. The findings of this study may contribute to and serve as the theoretical framework in developing investor behavior modeling that would analyze the motivational factors of real estate investment in the Philippines over time. Thus, real estate professionals, firms, and companies need to keep in mind the factors that affect the investment intentions of rising millennial and Gen Z investors. It is vital to ensure the sustainability and profitability of the properties they plan to purchase and/or construct to entice new investors, especially since transparency and accessibility are becoming increasingly necessary in the era of Industry 4.0.

4.2. Practical and Managerial Implications

This paper contributes to a better understanding of the motivational factors that influence investment intention and may lead to the investing behavior of the emerging generation of younger investors in the Philippines. The findings of this study can contribute to the decision makers and concerned parties in the Philippine real estate market from the following practical implications:

As previously stated, one of the key findings of this study was the insignificant influence of attitude, subjective norm, and perceived behavioral control on investment intention. The findings imply that millennial and Gen Z Filipinos of the middle-income class who intend to invest in the real estate market base their decisions not on their own opinions, the opinions of their peers, or their own capacity to invest. Considering that the main financing options for investing among the respondents were loans (i.e., Pag-IBIG and bank loans), it appears that practical factors are indeed more important to them when investing in real property than emotions and subjective opinions. This result has implications for the role of one’s socioeconomic status on investment intentions.

This study found that the perceived property value, risk tolerance, and aversion from illegal practice have a significant, positive, and direct effect on investment intention. The implication of this result is in line with the previous one, in that millennial and Gen Z investors of the middle-income class are more objective and rational in their decision making for investment. Furthermore, the respondents showed bias in investment intentions, expressing their preference to invest locally (i.e., in their region or area of residence). These results have implications for the consistency of the populace with the generally accepted principles of economic theory, further implying that these theories could be applied in the context of Philippine real estate and will arrive at the same conclusions.

Additionally, prior research has observed that investment behavior tends to change according to the business cycle [151,174]. This study has verified the reliability, validity, and usability of the proposed model for determining the factors of real estate investment intention and found the impact of inflation on it. Thus, researchers attempting to apply this model in their studies ought to consider the mediating effects of economic conditions during different business cycles. The reliability of the data they will collect would have practical implications on how key players in the real estate industry accurately determine the behavioral factors of their target market.

The validated factors that underlie the investment intention and possibly the behavior of middle-income millennials and Gen Zs in the Philippines during inflation can contribute to the decision makers and concerned parties in the Philippine real estate market from these managerial implications: (1) business cycle, (2) regional real estate valuation, and (3) fraud prevention.

Business cycle implication: The results of the proposed ETPB model would allow for the identification and verification of behavioral factors affecting the intention of Filipinos to invest in the real estate market. Government agencies such as the National Economic and Development Authority (NEDA) could also assess the mediating effect of socioeconomic factors on Filipinos’ investment intentions during different business cycles. Existing real estate companies, firms, and developers can rely on the results to understand and consider the behavior of their potential partner investors and target customers in their long-term visions and projects. Meanwhile, current and future real estate investors can refer to the results and study the intentions and possibly investment behavior of their competition when economic conditions are met. Moreover, with the consideration of fluctuations in the business cycle, the Monetary Board of The Bangko Sentral ng Pilipinas (BSP) could adjust the interest rates to fight and possibly leverage the effects of inflation on the country’s investment activities in the real estate industry. Governmental support through tax increases and updating policies, among others, will entice more of the younger generations to invest in real properties and would help develop more sustainable cities.

As it stands, the recent wage increases for Filipinos are enough to cover inflation and rising commodity prices and leave a little portion of their income to pay for housing [3]. Compounded by a formal market that predominantly caters to upper-class housing where profit is more secure, among other deficiencies, informal markets have emerged and developed to cater to slums and low-income families [173,175,176]. To help reduce the housing backlog, the Home Development Mutual Fund or Pag-IBIG Fund, one of the main financing options of the study respondents, lowered its interest rates for housing loans [177]. Meanwhile, the first quarter of fiscal year 2023 has been a fruitful period for current real estate investors as house prices, specifically residential properties, increased by 10.2% following the reopening of the Philippine economy [178], which could also mean more people will not be able to afford duplex housing units, single-detached or attached houses, townhouses, and condominium units. With these considerations along with the banking regulations, zoning ordinances, and government policies in the country, it is safe to assume that politics has a vital role in the real estate sector. The combined efforts of the different government agencies to provide affordable housing encourage the financialization of the housing market—that is, the increasing demand results in increased house prices so long as lower interest and mortgage rates give incentives for people to purchase and invest more. As the progress to address the housing backlog is considered inadequate [179], it stands to reason that focusing on improving the conditions that affect the behavioral intention of the middle class to invest in real estate, as seen in this study’s results, could have a big impact in this social issue.

Regional Real Estate Valuation: The home bias expressed by a majority of the study participants presents the importance of data to aid in their implied objective and rational investment decision making and intentions. Key players in the real estate market who utilize disruptive technologies, such as the members of the PropTech Consortium of the Philippines, could use the results of this model as a theoretical background in designing new solutions to property valuation. Real estate developers, firms, and companies, as well as other real estate professionals, could also use the significant factors to make better, data-driven managerial decisions for their target market and prospective investor partners. Moreover, reference to the larger effect of the millennials’ and Gen Zs’ perceived property value could partly be a basis for developing key performance indicators (KPIs) in determining which areas, cities, and regions are best, or viable at the very least, for real investment.

In Indonesia, barriers to using publicly accessible data management systems, such as the use of a Public Asset Management framework in one of their local governments, are the lack of a supporting institutional and legal framework, the number of areas they must manage, and the lack of data, among others [180]. Osunsanmi et al. [181] found that a decentralized real estate market and the management of big data sets using data-management systems are among the leading factors that explain the need for the application of data-science techniques by real estate professionals in Africa. Meanwhile, Renigier-Biłozor et al. [23] observed reluctance to accept modern automated tools for property valuation from traditional property appraisers, and stressed the importance of such solutions for overall sustainable development. Newell et al. [182] also highlighted the need to improve the use of Environment, Social, and Governance (ESG) benchmarking, with emphasis on the climate risks in different regions. Post-pandemic, more research has explored the application of data analytics, from combining spatial and temporal evolution using context-aware matrix factorization to understand regional markets [183], to using Chain-of-thought (COT) prompt engineering in generating property valuation reports [184].

Similar to the issues faced by Indonesia more than a decade ago, the Philippines have an inadequacy of data that can be used for property valuation. And if there were, the majority possibly do not have access to them or do not have a remote idea they exist and are available. Real estate professionals that are not affiliated with established companies and firms generally operate a brick-and-mortar business, and the country is still lagging in adopting digital technology. In line with the recommendation to improve on the factors that affect the investment intention of the middle class, part of the strategic approach to entice them is to have access to real-time and transparent regional data for easier decision-making. Adopting the methods mentioned above in the development of new systems will demonstrate remarkable capabilities in monitoring and evaluating the regional real estate market. Specifically, temporal and social evaluations for the regional real estate market, as well as the consolidation of KPIs and other relevant data from each region (e.g., natural features, resources, land characteristics), that can be deployed online are highly recommended.

Fraud-prevention implication: The apparent aversion of millennials and Gen Z Filipino investors of the middle-income class has more influence on their risk tolerance than their investment intention, although both their aversion to illegal practice and risk tolerance have significant, positive, and direct effects on this. At least with the economic conditions the respondents were subjected to during the data gathering period, one’s aversion to illegal practice had the least effect on their intentions to invest in real estate. While there may be caveats to these results, they suggest the low possibility of new younger investors circumventing policies, restrictions, and regulations in exchange for potential gains. Concerned members and stakeholder agencies of the National Task Force on Anti-Illegal Real Estate Practice could reference these results in their investigations regarding this issue and promulgate more effective and robust policies to address the presence of colorums. Moreover, educational institutions and government agencies, such as the Department of Education (DepEd) and the Commission on Higher Education (CHED), ought to promote the study of the country’s laws, policies, and regulations to provide at least the basic knowledge of them and further the advocacy against colorums and illegal real estate practices.

Addressing illegal land use and development has been a topic of interest in the academic literature. In China, one study by Lian et al. [38] investigated the effectiveness of Chinese local governments’ adoption and standardization of the market-led transactions (MLTs) approach—that is, lands sales will be conducted publicly—in reducing rampant illegal land use. The MLT approach features the importance of increased, if not full, transparency, which the study found to reduce cases of illegal land use and prevent corruption. Another study in China by Lin et al. [39] used presence-only maximum entropy (MAXENT) on land use data to estimate potential illegal land development. This is to address the problems brought about by traditional prediction methods. In Indonesia, Astuti et al. [185] designed an approach to visualize illegal palm oil plantations using spatial data and high-resolution satellite data as part of the country’s One Map Policy. In general, it is evident from previous studies that a combination of government intervention and the use of big data and technology plays a big role in curbing illegal real estate practices.

It is important to note, however, that China is already a developed country and Indonesia is a newly industrialized (developing), middle-income country that has the largest economy in Southeast Asia. The Philippines is a developing lower-middle-income country that is facing a persisting housing backlog in low-cost, economic, and socialized segments, with more than 50% unserved households, and the low effectiveness of low-cost housing projects for informal settler families (ISFs) because they are far from people’s workplaces [8]. Simply put, the resource gap in implementing centralized real estate operations and profiling and prevention models for illegal practitioners is harder and will take longer for the Philippines to address, unlike for China and Indonesia. In another middle-income developing country such as Brazil, a study by Guedes et al. [186] analyzed the housing supply with an extended monocentric city model using demographic data and public land ownership. The extent of this study was on housing supply estimation and elasticity. To this end, there are a lack of studies and progress regarding identification strategies for developers and real estate professionals practicing illegally. This further strengthens the need for transparency, extensive monitoring solutions, and the development of novel models for predicting illegal real estate activities.

4.3. Limitations and Future Research

The possible limitations of this study provide a basis for the recommendations for future studies, as these may significantly affect the study results and provide insufficient information for investment decision-making. The first limitation was the effect of uncontrolled variables, such as economic factors seen in the data collection timeline, on the resulting final model. This study gives a preview of the conditions by which the study participants were individually impacted at a single point in time. Halfway through Q2 and at the beginning of Q3, Filipinos were dealing with an increase in their household expenditure as a result of inflation. Since their purchasing power was distorted, coupled with low wages, the study participants were less likely to invest large amounts of money and were more focused on their necessities. Future studies could resolve this limitation through the following: (a) assess both the intention and the actual investing behavior of the target respondents; (b) conduct a longitudinal study on the intention and/or actual investing behavior of the target respondents; and/or (c) investigate the differences between the investing intention and/or of the target respondents during varying economic conditions.

The first approach would address the intention–behavior gap with investing in the Philippine real estate market. Through this, the moderating influence of other factors, such as the influence of green consumption, could also be examined. The second suggested approach would accurately identify investment intention in real estate since the market fluctuates over time, and the target respondents will have had the time to improve their financial literacy, broaden their market research, and other relevant factors that contribute to their behavioral intentions, so as to have varying opinions and/or change their thoughts regarding the matter over time. The third suggested approach would require a longer data-gathering period, since the researchers cannot accurately predict the market conditions. A solution to this would be to secure their respondents to measure their intention at the beginning of a period and record their investment behaviors after the specified duration.

The second limitation observed in the study is the demographic composition of the respondents. The results show that gender and generations delimited in the study are more or less equally represented, but the respondents are mostly concentrated in a few regions. Additionally, while it is acceptable for the majority to have a non-finance background, a significant majority of the respondents do not have experience in investing in real estate. To address these issues and other relevant demographic information, the following is recommended for future studies: (a) collect respondent and other relevant data from all regions; (b) apply a qualitative methodology; and (c) perform comparative examinations based on different socioeconomic factors.

The first approach would foremost allow the authors to gather a sufficient sample that was representative of the different regions in the country. The varied natural features, resources, and land characteristics present in the archipelagic states are some of the many reasons why the real estate market in the Philippines is highly regional. In addition to this, identifying relevant key performance indicators (KPIs) along with other environmental and sustainability factors is important to improve managerial implications, objectively assess the desired properties from different regions, and reduce potential bias. The results of the second approach may not represent the population being studied but would instead welcome the expertise of accomplished real estate investors and other real estate practitioners. Contrary to the quantitative methodology of the current study, this method would allow the exploration of the local real estate market in a manner as to expose and identify the motivational factors based on the regional contexts. An alternative method of data gathering would be interviews to gather in-depth market insights in line with the objectives. Lastly, the third suggested approach would be to conduct comparative examinations of young working adults from different cities and regions across the Philippines. There are potential benefits for commercial real estate firms as results can aid in their cost–benefit analyses. Additionally, considering the cultural constraints related to the cities, rural-urban areas, or regions, provides a new dimension to the study.

The third limitation concerns the coverage of risk tolerance and investing and/or financial knowledge in the model’s measurement items. The novelty of investing brought about partly by the development of the digital culture increased the frequency of high-risk investments. While the results of this study do not imply that the investment intentions of new, younger, and more diverse investors are led by their emotions, the results of the previous literature and those of recent surveys reflect otherwise. Although this finding might be purely hypothetical, the suggestion is to include the level of confidence the target respondents have in relation to their financial knowledge. Another is to include an assessment to measure the gap between their objective and subjective financial knowledge, as overestimation or underestimation of the latter has been found to affect one’s confidence levels, and by extension the level of their investment risk tolerance.

Another limitation observed in the study was the application of a single theoretical model, which was the theory of planned behavior (TPB). While the extended model is proven to be reliable and valid, the results of the hypothesis testing suggest that there may be other factors affecting the investment intention of millennials and Gen Zs. Future studies could address this limitation by applying a differing theory with relevant independent factors. Another option available is to apply multiple theories and identify other factors that have not yet been included under the confines of the theories applied, so as to create a more comprehensive conceptual framework that would explain the intention and/or behavior of Filipinos in the real estate market.

The application of such a model to identifying and predicting illegal real estate practices will naturally require ethical reviews and considerations. Furthermore, the presence of informed consent in the questionnaire detailing the study’s intent to predict illegal activities could deter most if not all real estate practitioners that are conducting shady business on social media platforms. Social constraints, such as government regulations and required licenses, are some of the entry barriers to real estate, yet some still employ the services of the younger and less informed to help with their online advertising. While this may no longer help in profiling illegal practitioners in an online setting, the results will reflect those who are legal, honest, and with ethical conduct. As such, these will help recommend solutions and policies that cater only to those registered professionals and deter illegal activities.

5. Conclusions

Filipinos have been dealing with significant barriers to homeownership, increasing mortgage and interest rates, and illegal real estate practitioners, on top of other social and economic issues the country has been facing for decades. As they gain more purchasing power and financial literacy in the rise of the digital landscape, investing in real estate is slowly proving to be a good option not only to increase their wealth but also to protect themselves against inflation. With the Philippines’ rapid growth in e-commerce and digital media, the government acted on its digital transformation to improve its service and economy. The Philippine real estate market has been leveraging disruptive technologies and social media to achieve the same goals. However, while the digital divide in the real estate market is bridged by disruptive technologies, illegal real estate practices remain prevalent, especially in social media platforms more accessible to the masses. The real estate market’s digital transformation highlights the use of data science techniques and big data analytics to mitigate risks and prevent fraud. A total of 400 millennials and Gen Zs from different regions in the country participated in this study to explore the relationship between personal attitude (AT), subjective norm (SN), perceived behavioral control (PBC), risk tolerance (RT), perceived property value (PPV), aversion from illegal practice (IP), and investment intentions (II) using an extended theory of planned behavior model (ETPB).

The objective of this study was to assess the model using partial least squares structural equation modeling (PLS-SEM) to determine the intention of millennials and Gen Zs to invest in real estate. The results showed that the model was sufficient to explain the risk tolerance (RT), subjective norm (SN), perceived behavioral control (PBC), and investment intention (II) of the respondents. The findings rejected the hypotheses that attitude, subjective norm, and perceived behavioral control all have a significant and direct effect on investment intention, despite the model’s reliability, validity, and fit. Considering the economic situation faced by the respondents during the data-gathering phase, the results imply that when experiencing inflation, the investment intentions of millennials and Gen Zs in the real estate market are not influenced by their attitude towards real estate, or the opinions of the people around them about investing in real estate, nor is it based on their self-efficacy to invest. Their intentions to invest in real estate are instead influenced by their perception of the property’s value, their personal risk tolerance, and their aversion to illegal practices.

This study only focused on the intentions of millennials and Gen Z to invest in the real estate market, specifically through physical (i.e., residential, commercial, house flipping, raw land) and non-physical (i.e., REIGs, REITs, online crowdfunding) real estate. Additional findings shed light on the impact of the state of the market on their investment intentions considering their respective socioeconomic status in the country. Since the country’s real estate industry is regarded as one of the biggest housing markets in Southeast Asia, key players and other concerned parties in the industry should take advantage of new disruptive technologies and improve their decision-making with big data analytics such as the application of this model. Further validation, improvement, and application of this model in the current market, unripe as it is, could contribute to furthering the progress in the country’s digital transformation as well as strengthening the real estate market.