Real Estate Values and Urban Quality: A Multiple Linear Regression Model for Defining an Urban Quality Index

Round 1

Reviewer 1 Report

1、The logic of the introduction part is not strong, which makes it difficult for readers to read.

2、Section 2 refers to "method" and Section 5 also refers to "method". Although their specific contents are different, this article layout is still confusing. Please consider whether they can be combined.

3、Section 3, merge it into section 2 as one of the sections, it may be more reasonable.

4、Line 132, the punctuation is missing.

5、Please consider whether it can be merged with other sections, and please make a more in-depth summary for Figure-1.

6、What is "The Statistical Model" in Section 5.1, please list. Consider putting equation 2 in 5.1. It is not appropriate to put the formula in the "result" section.

7、The results obtained in section 6 are too simple, please consider if you can dig deeper into valuable knowledge.

Author Response

1. The logic of the introduction part is not strong, which makes it difficult for readers to read.
   The logic of the article is to identify and quantify the elements that contribute to greater urban quality. For this reason, the property values were observed because the price of the properties is the result of the appreciation of the buyers with respect to some intrinsic and extrinsic characteristics, that is, of the urban context where the properties sold are located.
2. Section 2 refers to "method" and Section 5 also refers to "method". Although their specific contents are different, this article layout is still confusing. Please consider whether they can be combined.
   The manuscript was developed following the "Instructions for Authors" proposed by the publisher.
   We agree with the proposal to combine the sections.
   
3. Section 3, merge it into section 2 as one of the sections, it may be more reasonable.
   Sections 2-3-4-5 have been merged
   
4. Line 132, the punctuation is missing.
   Punctuation has been added
5. Please consider whether it can be merged with other sections, and please make a more in-depth summary for Figure-1
   Figure-1 is a graphical representation of the data collected for processing the regression model. The circle charts immediately show the relationships between the microzones analyzed (M1, M2 ... etc.) and the entire city of Pescara.
   
6. What is "The Statistical Model" in Section 5.1, please list.
   "The Statistical Model" has been changed to "Evaluation model"
   Consider putting equation 2 in 5.1. It is not appropriate to put the formula in the "result" section.
   The equation has been removed from the "Results" section. 

Reviewer 2 Report

Provide a more detailed review of the research work done in the topic. 
Figure 1 is not easy to read. It would be better to use bar graphs.
Does the statistical model work with weights? Each measurement works with other orders of units (for example, Environmental System vs. Services System).
Figure 2 does not represent a normal probability distribution. Add static calculation and verification.
The regression analysis did not perform a non-correlation test of the input values.

No future research is suggested.

Author Response

1. Figure 1 is not easy to read. It would be better to use bar graphs.
   Circle charts have been elaborated because, according to the authors, it better represents the data of the single micro-zone studied compared to the total data of the City of Pescara.
   
2. Does the statistical model work with weights? Each measurement works with other orders of units (for example, Environmental System vs. Services System).
   In general, the model doesn't work with weights; however, for some variables a hierarchy was made according to the degree of appreciation of the residents.
   As reported in "Settlement System", the two sub-indexes (Building Quality Score and Building Typology) reference was made to a previous study by the authors from 2012.
3. Figure 2 does not represent a normal probability distribution. Add static calculation and verification.
   Normality tests have been inserted (Shapiro-Wilk e Kolmogorov-Smirnov) which show how the distribution can be considered normal
   
4. The regression analysis did not perform a non-correlation test of the input values.
   The correlation test was performed and did not reveal significant relationships between the variables
   
5. No future research is suggested.
   in "Discussion" describes how the model can be used in tax matters. The Urban Quality Index thus constructed can represent an innovative tool at the service of the political decision maker and / or public administration to operate a fiscal rebalancing among taxpayers and at the same time suggest more balanced planning strategies 

Reviewer 3 Report

Overall Recommendation: Major revision

 

Title

The title looks confusing. Are you trying to calculate the Urban Quality Index by using a regression model? Or, do you want to stress that the calculated Index significantly explains the housing price movement?

 

Introduction

Here, the paper fails to provide the original value of research to the international academic society. You mentioned that the paper would provide an insight on the mechanics of adaptive reuse of redevelopment, but the final analysis seems to be irrelevant to that point. Why do you look at the Abruzzo housing market, and what does your analysis provide about the housing market operation? In addition, the paper lacks the theoretical discussion on hedonic pricing models. Cite Rosen(1974)(Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition) or other related works.

 

Results

The regression analysis omitted some important variables, which determines housing values. First of all, you need to control for residential density. We expect that density, all else equal, is negatively related to housing prices because dense environment is more likely to result in congestion and the invasion of privacy. Second, building age is one of the most important variables in hedonic pricing model. You need the completion date and the date sold for each housing unit. Then, you need the age squared. Consult the paper by Bokhari & Geltner(2018), published in Real Estate Economics. How did you calculate the building age variable? Third, you should add the (monthly) time trend variable. Suppose the earlies date sold is February, 2010. Then, the observation takes the value of 1 for the time trend variable. It is 2 for the units sold in March, 2010. Finally, you need three seasonal dummy variables to control for the seasonality.

Your exhibition of the statistical results is unacademic. Do not copy and paste the results from SPSS onto the paper. Please consult some peer-reviewed academic papers, which provides the results of multiple regression analysis.

We researchers call the calculated value “the predicted value”.

You emphasize the creation of the Urban Quality Index. How much does the Index contribute to capturing the housing price movement? Please show a metric to address it, not the t statistic for the Index.

Also, you need to provide descriptive statistics for all variables used in the regression.

Author Response

The title looks confusing. Are you trying to calculate the Urban Quality Index by using a regression model? Or, do you want to stress that the calculated Index significantly explains the housing price movement?
The study seeks to identify and quantify the elements that contribute to the definition of greater urban quality. For this purpose, the reading of real estate values through a multiple linear regression model allows us to confirm the hypotheses assumed by the authors.
Here, the paper fails to provide the original value of research to the international academic society. You mentioned that the paper would provide an insight on the mechanics of adaptive reuse of redevelopment, but the final analysis seems to be irrelevant to that point. Why do you look at the Abruzzo housing market, and what does your analysis provide about the housing market operation? In addition, the paper lacks the theoretical discussion on hedonic pricing models. Cite Rosen(1974)(Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition) or other related works.
The study was conducted on the real estate market of the City of Pescara as the authors, working in this context, were able to acquire many deeds of sale necessary for the construction of the valuation model (which is not possible for other cities). Rosen was mentioned.
The regression analysis omitted some important variables, which determines housing values. First of all, you need to control for residential density. We expect that density, all else equal, is negatively related to housing prices because dense environment is more likely to result in congestion and the invasion of privacy
Aspects of density were considered indirectly through the development of the "building construction quality subsystem" (settlement Macrosystem). In fact, the greater housing density has been identified with a greater presence of tower and multi-storey buildings.
Second, building age is one of the most important variables in hedonic pricing model. You need the completion date and the date sold for each housing unit. Then, you need the age squared. Consult the paper by Bokhari & Geltner(2018), published in Real Estate Economics. How did you calculate the building age variable?
The age of the building was considered in its intrinsic characteristics (Building Age Index), discretizing the data with values ranging from 0 (for new or renovated buildings) to 4 for old buildings built after 1976
Your exhibition of the statistical results is unacademic. Do not copy and paste the results from SPSS onto the paper. Please consult some peer-reviewed academic papers, which provides the results of multiple regression analysis.
The solution proposed by the auditor was used: only the "Table of Coefficients" was left.
We researchers call the calculated value “the predicted value”.
The wording in "the predicted value" has been corrected
You emphasize the creation of the Urban Quality Index. How much does the Index contribute to capturing the housing price movement? Please show a metric to address it, not the t statistic for the Index. Also, you need to provide descriptive statistics for all variables used in the regression
The urban quality index does not help to define prices: the price of the properties is formed by a series of intrinsic characteristics but also by extrinsic characteristics expressed in the form of the urban quality index (UQI). Since the urban quality index appears significant and relevant in the formation of the price, the authors believe that its elaboration can suggest to administrators how to operate in the future in order to improve urban quality in the different districts of the city (for example by increasing greenery, or the number of bus lines, or parking lots etc etc). 

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

It would be fine if you provide short explanations how and where important hedonic variables, such as age, age squared, density, and so forth, are included in your index variables. 

You should add the (monthly) time trend variable because the predicted variable has a trend. You have to remove the trend by including the time trend variable.

The second version of paper seems to contain a broken table (Table 10) and some typos (line 488). In addition, please use "p" or "p-value" instead of "Sign." in Table 12.

Again, please add the time trend in your regression. I will look into it in the next round of revision. 

 

Author Response

Dear Reviewer,

Thank you for providing us with your valuable comments.

Our response is attached to this document

Author Response File: Author Response.pdf

Round 3

Reviewer 3 Report

The paper will be able to be accepted in current form if you include in the final version your responses about the variables description and the time trend. I insist that you include your partial analysis that shows there is no significant price trend in the Appendix section. Thank you.