Peer-to-Peer Lending as a Determinant of Federal Housing Administration-Insured Mortgages to Meet Sustainable Development Goals
Round 1
Reviewer 1 Report
“Peer-to-peer lending as a determinant of FHA-insured mortgages in order to meet SDGs”
Manuscript ID: sustainability-2543180
Journal Sustainability
Referee’s Report
This paper examines the influential factors of Federal Housing Administration (FHA) mortgages volume, focusing mainly on peer-to-peer (P2P) lending. The authors employ data over the period 2007-2017 for all the 50 US states and use panel data techniques to analysis the relation between FHA mortgages, crowdlending and other economic factors.
The paper presents valuable research and contributes to the finance literature relating to residential mortgage lending and P2P lending. The writing style is concise and easy to follow, making it accessible to a wide readership. The data analysis is thorough and effectively addresses the research question, i.e. the ability of P2P to bypass mortgage supply constraints (tightened LTV caps), yielding meaningful and statistically significant results. Since the originality of the research is well established, it should of interest to the readership of the journal and therefore I recommend the manuscript for acceptance, pending the authors' consideration of the revisions mentioned below.
Loan origination behavior in the post-crisis period
The presentation of the volumes of both mortgage and P2P loans is complete and the figures and tables show essential data. The trends can be seen support the discussion and conclusions of the paper, but it could be useful to add a table with the number and the amount of loans (FHA and P2P loans) issued by each state for each year under study, for purposes of information and completeness of statistical analysis mainly.
Empirical Results
Since the descriptive statistic table capture many features of the selected dataset, it would be a good idea to include the median as well. Showing both measures (mean and median) can give a more complete picture of the central tendency, especially if there are outliers that significantly affect the mean.
Minor comments
1) Authors should specify which variables are in volume and which in percentages %
2) Authors should remove from the introduction the following phrase and add at the empirical model section: “The volume of FHA mortgage loans is the dependent variable of our model and the variables: Interest rate, per capita GDP, Unemployment rate, House Price Index, New Residential Housing Units, Population growth, Broad Money and Consumer Confidence Index are the independent variables.”
Please check for typos errors
Author Response
Peer-to-peer lending as a determinant of Federal Housing Administration-insured mortgages to meet Sustainable Development Goals”
Manuscript ID: sustainability-2543180
Response to the Referee 1
We would like to thank the referee for carefully reviewing our manuscript and for providing valuable comments. We appreciate very much your careful and thorough review of our paper, which resulted in valuable feedback that significantly improved our work. In consideration of your comments, we have made substantial revisions to the paper. As per your suggestions we have improved and expanded the theoretical and empirical sections of the paper. We have made every effort to address all of your concerns to the best of our ability. Below are our responses to the specific comments raised in your report. For ease of reference, the original comments are summarized below in italics.
Loan origination behavior in the post-crisis period
The presentation of the volumes of both mortgage and P2P loans is complete and the figures and tables show essential data. The trends can be seen support the discussion and conclusions of the paper, but it could be useful to add a table with the number and the amount of loans (FHA and P2P loans) issued by each state for each year under study, for purposes of information and completeness of statistical analysis mainly.
In line with the recommendation of the referee, we have included two additional tables, Table A1 and Table A2, in the Appendix section. These tables provide information on both the number and volume of originated loans, with Table A1 concerning FHA loans and Table A2 P2P loans.
Empirical Results
Since the descriptive statistic table capture many features of the selected dataset, it would be a good idea to include the median as well. Showing both measures (mean and median) can give a more complete picture of the central tendency, especially if there are outliers that significantly affect the mean.
In accordance with the referee's suggestion, we have introduced a new column into the descriptive statistics table on page 14, immediately following the "mean" column. This new column displays the median values for the selected variables.
Minor comments
1) Authors should specify which variables are in volume and which in percentages %.
We have incorporated an additional column into Table 2, titled "selected variables" on page 12, which now presents the specific values for each variable.
2) Authors should remove from the introduction the following phrase and add at the empirical model section: “The volume of FHA mortgage loans is the dependent variable of our model and the variables: Interest rate, per capita GDP, Unemployment rate, House Price Index, New Residential Housing Units, Population growth, Broad Money and Consumer Confidence Index are the independent variables.”
Following the referee’s suggestion, we have made this modification in the text.
In closing, we wish to thank you again for your invaluable comments and suggestions regarding our paper. We believe that the revised manuscript has undergone substantial improvement thanks to your input, and we sincerely hope that our explanations and revisions meet with your satisfaction.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The study design is well-suited, employing a panel data approach, which is appropriate for the research objectives. The empirical results of this research are significant and support the initial hypothesis, i.e. the positive relation between the crowdlending and FHA mortgages.The paper offers new insights, highlighting the dynamic of P2P lending to circumvent mortgage supply constraints and thus inequalities can be reduced and Sustainable Development Goals can be achieved. Therefore, given paper’s significant contribution, it should be of interest to the journal's readership.
The introduction provides a good, generalized background of the topic. However, in order to make the introduction more complete and differentiate the paper some more, the authors should also include relevant recent studies (if possible) that link the evolution of mortgage loans to emerging technologies, especially after the financial crisis.
In table 5 the confidence levels of 1%, 5% and 10% should be indicated (as is also the case in the tables of the empirical analysis that follow). Brief mention should also be made to the independent variables that have the highest correlation coefficients.
Moreover, consider assessing normality using statistical test (such as the Shapiro-Wilk test). In panel data analysis, it is essential to assess the normality of the residuals (error terms). Normality test can help identify potential outliers in the data and provides insights into the adequacy of the model specification.
Remember that normality of the error term is an assumption of many linear regression models. If the normality test indicates non-normality, you may need to consider other regression techniques.
Authors should read carefully throughout the paper in order to fix some typos especially at the abstract: e.g. in payment the letter “t” is missing…etc
Author Response
Peer-to-peer lending as a determinant of Federal Housing Administration-insured mortgages to meet Sustainable Development Goals”
Manuscript ID: sustainability-2543180
Response to the Referee 2
We would like to thank the referee for carefully reviewing our manuscript and for providing valuable comments. We appreciate very much your careful and thorough review of our paper, which resulted in valuable feedback that significantly improved our work. In consideration of your comments, we have made substantial revisions to the paper. As per your suggestions we have improved and expanded the theoretical and empirical sections of the paper. We have made every effort to address all of your concerns to the best of our ability. Below are our responses to the specific comments raised in your report. For ease of reference, the original comments are summarized below in italics.
The introduction provides a good, generalized background of the topic. However, in order to make the introduction more complete and differentiate the paper some more, the authors should also include relevant recent studies (if possible) that link the evolution of mortgage loans to emerging technologies, especially after the financial crisis.
In compliance with the referee's guidance, we have included the subsequent sentence on page 5 to address their suggestion:
“……Several recent studies have examined the U.S. residential mortgage market in light of new technological achievements and show that traditional methods of lending may no longer be common practice. For example, Ref. [16] studies how the use of digital footprint is reshaping the mortgage market and shows that the number of lenders that use a borrower’s digital footprint has remarkably increased and these users bear a significantly lower risk compared to non-users. Ref. [17-18] show that FinTech lenders dramatically increased their market share of U.S. mortgage lending, suggesting that technological innovation has improved the efficiency of financial intermediation in the U.S. mortgage market.….”
In table 5 the confidence levels of 1%, 5% and 10% should be indicated (as is also the case in the tables of the empirical analysis that follow). Brief mention should also be made to the independent variables that have the highest correlation coefficients.
We agree with the referee. We have made the appropriate changes in table 5 “correlation matrix of all variables” on page 15 and we also commented on the results by including the following paragraph:
“…Our results show that there is a highly negative relationship between interest rate (IR) and unemployment rate (UR) and a highly positive correlation between IR and the broad money growth (BM). The inflationary pressure that the BM creates justifies this highly positive correlation with the IR variable i.e., a higher inflation rate explains a higher interest rate. On the other hand, job loss and the inability of people to save or borrow money may also justify this highly negative correlation with the interest rate i.e., the higher the UR the lower the IR.
After that, we observe a highly negative correlation between UR and the consumer confidence index (CCI) (-0.5179). A possible explanation is that when the unemployment rate increases, the optimism that the CCI captures decreases. Concerning the positive correlation between GDP per capita and house price index (HPI) (0.4670), it can be explained by the following: as GDP per capita increases, personal income increases as well, which means that the demand in housing market may also rise, thus increasing house prices”.
Moreover, consider assessing normality using statistical test (such as the Shapiro-Wilk test). In panel data analysis, it is essential to assess the normality of the residuals (error terms). Normality test can help identify potential outliers in the data and provides insights into the adequacy of the model specification.
Remember that normality of the error term is an assumption of many linear regression models. If the normality test indicates non-normality, you may need to consider other regression techniques.
This is a valid point. We conducted a normality test using the Shapiro-Wilk test in Stata, and we have included the following paragraph on page 18:
“Furthermore, we checked the normality of residuals since it helps to ensure that the model’s assumptions are met and that the data fits well to our model. The assumption is that the error terms are normally distributed, thus the estimators are efficient and have desirable statistical properties, such as minimum variance. To assess normality, we use the statistical test of Shapiro-Wilk. The results of the test indicate that the residuals are normally distributed because the p-value is greater than the conventional significance level of 0.05”.
Authors should read carefully throughout the paper in order to fix some typos especially at the abstract: e.g. in payment the letter “t” is missing…etc
We have corrected typographical errors throughout the entire text.
In closing, we wish to thank you again for your invaluable comments and suggestions regarding our paper. We believe that the revised manuscript has undergone substantial improvement thanks to your input, and we sincerely hope that our explanations and revisions meet with your satisfaction.
Author Response File: Author Response.pdf
Reviewer 3 Report
In general, I think that the article "Peer-to-peer lending as a determinant of FHA-insured mortgages in order to meet SDGs" is a very interesting and enriching piece of research.
With specific regards to the empirical assessment, I do not have particular comments since I think that the aithors have done a good job in conveying their findings.
What is, in my opinion, less acceptable is 1) the overuse of abbreviations - two in the title and "LTV" in the abstract without defining it - and 2) the high number of typos and/or grammatical inconsistencies. For instance, "paymen", "P2P lending have", "as a mean to reduce", "the first that examine", "the use of different dataset", "morgages", "A lot of studies" and several others. This aspect should be urgently fixed, because it distracts the reader from the paper's content.
See above.
Author Response
Peer-to-peer lending as a determinant of Federal Housing Administration-insured mortgages to meet Sustainable Development Goals”
Manuscript ID: sustainability-2543180
Response to the Referee 3
We would like to thank the referee for carefully reviewing our manuscript and for providing valuable comments. We appreciate very much your careful and thorough review of our paper, which resulted in valuable feedback that significantly improved our work. In consideration of your comments, we have made substantial revisions to the paper. As per your suggestions we have improved our paper. We have made every effort to address all of your concerns to the best of our ability. Below are our responses to the specific comments raised in your report. For ease of reference, the original comments are summarized below in italics.
What is, in my opinion, less acceptable is 1) the overuse of abbreviations – two in the title and “LTV” in the Abstract without defining it and 2) the high number of typos and/or grammatical inconsistencies.
In line with referee’s comments we have replaced the abbreviations from the title with their full word counterparts, and we have rectified all typos and grammatical inconsistencies throughout the entire text.
In closing, we wish to thank you again for your invaluable comments and suggestions regarding our paper. We believe that the revised manuscript has undergone substantial improvement thanks to your input, and we sincerely hope that our explanations and revisions meet with your satisfaction.
Reviewer 4 Report
-FHA-insured mortgages refer to home loans that are backed by the Federal Housing Administration (FHA). These types of mortgages are designed for people who have downgraded credit scores. Lower down payment requirements, less stringent credit requirements are some of their properties. Peer-to-peer (P2P) lending is one of the types of social lending, where the online platforms are usually benefited. It allows for an access to credit, diversification but has some risks compared to the traditional lending. The paper seeks for an effect of the P2P on FHA-insured mortages for the US economy.
-Principal-agent equilibrium model of Holmstrom and Tirole (1997) indicates a positive effect of P2P on that. Some other macroeconomic and financial variables also influence the montage volumes. Theoretical motivation of the paper is not strong and there are deficiencies such as lack of calibration. The transition from the theoretical model to panel data estimation is not motivated sufficiently. Discussion of the results is very limited compared to the length of paper. Conclusion section is not written analytically. I suggest authors to work more on the paper and improve it.
Others
-There are typo mistakes such as in the line15 : ” mortgage loafns”.
English language of the paper should be improved.
Author Response
Peer-to-peer lending as a determinant of Federal Housing Administration-insured mortgages to meet Sustainable Development Goals”
Manuscript ID: sustainability-2543180
Response to the Referee 4
We would like to express our gratitude for the referee’s dedicated time and effort in reviewing our paper. We appreciate very much your careful and thorough review of our manuscript, which resulted in valuable feedback that significantly improved our work. In consideration of your comments, we have made substantial revisions to the paper. In accordance with your suggestions we have improved and expanded the theoretical framework of the paper. We have made every effort to address all of your concerns to the best of our ability. Below are our responses to the specific comments raised in your report. For ease of reference, the original comments are summarized below in italics.
Principal-agent equilibrium model of Holstrom and Tirole (1997) indicates a positive effect of P2P lending on that. Some other macroeconomic and financial variables also influence the mortgage volumes. Theoretical motivation of the paper is not strong and there are deficiencies such as lack of calibration. The transition from the theoretical model to panel data estimation is not motivated sufficiently.
This is a valid point. We have revised the paper by adding the following paragraphs in section IV (pages 10 and 11) in order to link the theoretical model to our empirical analysis in section V.
“…The analysis is based on the framework of the Holstrom and Tirole (1997) model [41] which allows us to formulate our key empirical prediction: the impact of P2P lending on the effects of changes in Loan to Value caps and mortgage down payment borrower requirements as in Braggion et al. (2019). The rise in down payment requirements from traditional lenders is analogous to a “collateral squeeze”, which restrains credit in Holmstrom and Tirole’s model. We show that the availability of P2P lending allows borrowers to bypass the important LTV cap, neutralizing its effects such that the levels of new credit are not reduced. These results allow us to formulate the empirical prediction for our test.”
“Therefore, this analysis allow us to formulate the hypothesis that the P2P lending has a positive effect on the FHA mortgage origination which we will test in section V.”
Discussion of the results is very limited compared to the length of paper. I suggest authors to work more on the paper and improve it.
In response to the referee's valuable suggestion, we have thoroughly revised and enhanced Section VI “Presentation and Discussion of Empirical Results" and we have now included a lengthier discussion of the results in the paper (pages 18-19). “…The main aim of our study is to examine the relationship between the volume of FHA mortgages and P2P lending, considering various economic and financial factors, and the possibility of financial inclusion of underserved borrowers.
Indeed, based on our results, the initial hypothesis of our study that there is a positive relationship between crowdlending and FHA mortgages is verified. P2P lending has a positive and statistically significant (at the 1% level) impact on the volume of FHA mortgages that is in line with the existing literature supporting that FinTech lending has a significant impact on the housing market. However, among the studies that investigate FinTech lending as a substitute for traditional mortgage lending (banks), this study is the first to examine P2P as a source of financing for subprime borrowers to obtain FHA mortgages from conventional banks. Based on our analysis, P2P lending could be a strong alternative for people that do not fulfill the typical criteria required to obtain a FHA mortgage for purchasing their house. As a result P2P reinforces the housing market and the economy as a whole contributing to its sustainability.
Another result that is also strictly related to sustainability, is the highly positive and statistically significant impact (coefficient 0.969) of the GDP per capita variable on FHA mortgages. This finding is in line with various studies in the literature such as Ref. [42] and Ref. [46] and shows that a unit increase of the GDP per capita drives to a significant increase in the volume of FHA mortgage loans. “GDP per capita” is a crucial variable in our analysis and its increase is directly related to the sustainability of the economy as regards economic growth, higher income, and improvement of people’s welfare that may also drive an increase in the housing demand.
The population growth is another important factor in our study that also has a positive and significant impact (coefficient +0.5811) on FHA originations and promotes sustainability in terms of economic growth, housing demand and improvement of living conditions. This result is straightforward since the greater the population the greater the housing needs, and is also shown in the existing literature such as Ref. [49], the results of which are directly comparable to ours (coefficient +0.337).
A positive relationship is also observed between the volume of FHA mortgages and the New Residential Housing Units. This result is strictly related to the law of supply and demand. If the market functions well, then an excess supply of houses lowers the house prices, boosts the housing market and the economy growth. So, we can safely conclude that if the market functions well, a positive relationship between FHA mortgages and New Residential Housing Units contributes to the sustainability of the economy in terms of economic growth and welfare.
Other important variables in our analysis that also significantly affect the volume of FHA mortgage loans in a negative way are Board Money growth, Unemployment Rate and Interest Rate. Studies such as Ref. [51] and Ref. [52] concerning money supply, the Ref. [53], concerning interest rate, Ref. [47] and Ref. [48], concerning the unemployment rate among others, indicate similar results to our study and show this negative relation between those variables and the volume of mortgages. An increase of these variables drives to a significant decrease in the volume of FHA mortgages and actually harms the sustainability of an economy. More specifically, an increase of the interest rate implies an increase in the cost of borrowing: an increase of the unemployment rate reinforces the sense of uncertainty to people lowering their willing to spend or to borrow and the Board Money growth increases the inflationary pressures. Thus, an increase of these variables lowers the demand in housing market, increases the poverty and the income discrepancies among segments of the population and harms the sustainability.”
Conclusion section is not written analytically.
This is a valid point. We have added the following paragraphs (page 20); “Based on our results from the panel data analysis, we conclude that P2P lending, the GDP per capita, Unemployment rate, Interest rate, New Residential Housing Units, Population growth, Broad Money growth rate, and the Consumer Confidence Index are significant determinants of the volume of FHA mortgages. More specifically, we show that there is a positive and significant relationship between the volume of FHA mortgages and the P2P lending, GDP per capita, Population growth, and New Residential Housing Units and a significant negative relationship with Unemployment rate, Interest rate, Consumer Confidence Index and Broad Money growth rate.
An interesting result of our analysis is that we considered P2P lending as a factor of FHA loan volume, since P2P loans are used as a source of financing the increased down payment requirements. The empirical evidence of this study supports the hypothesis that an increased volume of the P2P loans has a positive and significant impact on FHA mortgages for the period 2007-2017, in line with the SDGs for sustainable finance and economic inclusion, adopted by the United Nations.
P2P lending has various financial advantages such as flexibility, a simplified process, speed, lower lending standards, and higher returns for investors. Beyond the financial advantages, P2P lending also creates social impact. In this context, crowdlending can provide financial resources to underserved people that have limited or no access to traditional banking system due to their lack of credit history, lack of collateral, or other barriers. In this way, poverty, economic inequalities and regional economic disparities may be reduced, and the sustainable development with social and economic equity may be promoted.
In our paper we show that P2P lending can contribute to a country’s sustainable development goals, mainly through the financial inclusion. Thus, such an analysis about the relationship between the volume of FHA mortgages and various economic and financial factors, with an emphasis on P2P lending, is directly related to the notion of sustainability.
Nonetheless, it's crucial to note that while crowdlending has the potential to contribute to sustainable growth, there are also risks associated with it, such as borrowers defaulting and regulatory complexities. Thus, it is recommended that policy-decision makers establish a regulatory framework for the crowdlending market, analogous to that of traditional banking system, and an effective mechanism for assessing and managing risks.”
Finally, we would like to thank you again for your comments and suggestions on our paper. We believe that the revised paper is now significantly improved as a result of your comments and suggestions. We hope that our explanations and revisions are satisfactory to you.
Author Response File: Author Response.pdf
Round 2
Reviewer 4 Report
Authors improved the paper sufficiently.
Language of the paper is sufficient.
