Climate policy uncertainty and its impact on real estate market dynamics: A sectoral and regional analysis

Chulyoung Cho; Jinseok Yang; Beakcheol Jang

doi:10.1371/journal.pone.0311688

Peer Review History

Original SubmissionJune 16, 2024
12 Aug 2024 Decision Letter - Marco Maria Sorge, Editor PONE-D-24-24485Climate Policy Uncertainty and Its Impact on Real Estate Market Dynamics: A Sectoral and Regional AnalysisPLOS ONE Dear Dr. Jang, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. ============================== Please submit your revised manuscript by Sep 26 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: No Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The manuscript examines the predictive ability of the Climate Policy Uncertainty (CPU) index in forecasting volatility across quoted real estate asset classes. The main results suggest that the CPU index, as provided by Gavriilidis (2021), is particularly effective in predicting volatility in industrial assets, followed by residential or property types such as apartments, residences, manufactured homes, retail spaces, and malls. Industrial assets, characterized by significant energy demand and emissions, are especially susceptible to the CPU index due to their exposure to energy price volatility and regulatory frameworks. The analysis is also performed using cross-sectional US regional data and multi-step time horizons. To achieve these goals, the paper provides an econometric assessment of the interplay between the realized volatility of quoted market indices and the CPU index. Specifically, it compares the results of an augmented AR(1) model, which incorporates the CPU index as a control variable, with those of a baseline model that is essentially a univariate autoregressive model on realized volatility. In my view, the paper lacks a thorough analysis of the econometric model. First, there is no evidence of a model selection procedure for choosing the augmented and baseline models. This task should be mandatory, especially in selecting the order of the autoregressive process. Actually, if the model is bad-specified, any further efforts of measuring the predictive ability of CPU may be unhelpful and the Diebold and Mariano test can be misleading. Instead, the authors follow the approach of Paye (2012) without providing a justification for why the AR(1) model is better suited to the data. Another point of concern is that the authors only focus on the comparison of the base model with the extended model without showing preliminary results of the univariate regressions. In my opinion, this omission prevents the reader from fully understanding the magnitude and relationship of the estimated beta parameters with the real estate indices. In addition, other controls may be included in the extended and base models to capture other dynamics in the considered real estate indices, such as market risk, interest rate risk, and credit risk. Furthermore, the “Robustness Check” section provides an analysis of the heteroscedasticity and autocorrelation of residuals. Regarding the former, the authors show that, in general, there is no heteroscedasticity, except for the "industrial model," where they use HC3 to adjust standard errors. Regarding autocorrelation, they perform the Durbin-Watson (DW) test, testing only for 1-lag, with no autocorrelation found. Citing the authors: "Results from the DW test for all models approached a value of 2, indicating an absence of serial correlation." However, nothing is done about higher-order autocorrelation. The Breusch-Godfrey procedure can be performed to test for higher-order residual autocorrelation, and the heteroskedasticity and autocorrelation consistent (HAC) covariance estimator should be considered. Additionally, a graphical approach to residuals inspection may help readers better understand the nature of the results. I suggest completing the econometric analysis of the manuscript to thoroughly explain the findings. REFERENCE Gavriilidis, Konstantinos. Measuring climate policy uncertainty. Available at SSRN 3847388 (2021) Paye, B.S., 2012. ‘Déjà vol’: predictive regressions for aggregate stock market volatility using macroeconomic variables. J. Financ. Econ. 106 (3), 527–546 Reviewer #2: The topic and analysis is robust. Literature review can be enhanced. Examples below. The conclusion should have a policy recommendation. 1.Maurizio d’Amato, Asma Salman, Giulia Mastrodonato and Giampiero Sirleo. (September 2022) Measures of Variability in the Application of Cyclical Capitalization (normal form) of the London Market. Springer Book chapter from the Book Property Valuation and Market Cycle. Book Chapter indexed in Scopus and Clarivate Analytics https://link.springer.com/book/10.1007/978-3-031-09450-7 ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0311688.r001
Revision 1
20 Sep 2024 Author Response PONE-D-24-24485 - Reviewer 1 Climate Policy Uncertainty and Its Impact on Real Estate Market Dynamics: A Sectoral and Regional Analysis Thank you for another opportunity to revise our manuscript. Our revision again benefitted from your and the editor’s writing guidance. We made a sincere effort to address all of the comments we received and, as a result, have made substantial writing changes to the paper. We hope that these changes and clarifications significantly improve the paper and alleviate the concerns you and the reviewer raised about the previous draft. We look forward to your feedback and would, of course, be happy to make further changes. Below we provide responses to your comments on our paper (reproduced in bold, blue italics). Comment 1: There is no evidence of a model selection procedure for choosing the augmented and baseline models. This task should be mandatory, especially in selecting the order of the autoregressive process. Actually, if the model is bad-specified, any further efforts of measuring the predictive ability of CPU may be unhelpful and the Diebold and Mariano test can be misleading. Instead, the authors follow the approach of Paye (2012) without providing a justification for why the AR(1) model is better suited to the data. Author Response: Thank you very much for your insightful feedback and for highlighting the importance of a rigorous model selection procedure. We fully agree that ensuring the proper specification of our models is critical for the robustness of our results, particularly when assessing the predictive ability of CPU and conducting the Diebold and Mariano test. In response to your concern, we conducted a thorough model selection process to determine the optimal order of the autoregressive (AR) process for both the augmented and baseline models. Specifically, we compared the performance of AR(1), AR(2), and AR(3) models using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Hannan-Quinn Criterion (HQC) across various specifications. We’ve added suggested content as below; [Line 129 (p.6) ~ Line 138 (p.7)] “ The decision to utilize the AR(1) model was based on a rigorous model selection process. We compared the performance of AR(1), AR(2) and AR(3) models using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Hannan-Quinn Criterion (HQC). Table 1. shows the comparison of AIC, BIC and HQC values for AR(1), AR(2) and AR(3). Across all model specifications, the AR(1) model consistently demonstrated the lowest AIC, BIC and HQC values, indicating that it provides the best balance between model fit and complexity. These finding confirm that the AR(1) model is the most appropriate for capturing the dynamics of our dataset, minimizing the risk of overfitting while enabling robust and reliable results.” Table 1 Comparison of AIC, BIC, and HQC values for AR(1), AR(2), and AR(3) Models Across Different Real Estate Indices Model Model AR(1) AR(2) AR(3) AIC BIC HQC AIC BIC HQC AIC BIC HQC AR-CPU-Comp. -1423 -1413 -1419.37 -1295 -1285 -1291.4 -1221 -1211 -1216.62 AR-CPU-Apart. -1457 -1447 -1453.26 -1334 -1324 -1329.89 -1275 -1265 -1271.09 AR-CPU-Ind. -1376 -1366 -1371.63 -1212 -1203 -1208.44 -1120 -1110 -1116.23 AR-CPU-Ret. -1304 -1294 -1299.81 -1135 -1125 -1131.23 -1017 -1008 -1013.44 AR-CPU-Mall. -1248 -1238 -1243.98 -1108 -1098 -1103.69 -1017 -1008 -1013.47 AR-CPU-F.Outlet -1325 -1315 -1321.17 -1249 -1239 -1244.96 -1174 -1164 -1170.23 AR-CPU-Hotel. -1269 -1259 -1265.17 -1165 -1155 -1160.62 -1085 -1075 -1080.57 AR-CPU-Mfchome. -1433 -1423 -1429.2 -1281 -1271 -1276.89 -1185 -1176 -1181.35 AR-CPU-Office. -1390 -1380 -1385.73 -1233 -1223 -1228.82 -1129 -1119 -1125.25 AR-CPU-Resident. -1399 -1389 -1395.38 -1220 -1211 -1216.52 -1108 -1098 -1103.99 As shown in the table, the AR(1) model consistently yields the lowest AIC, BIC, and HQC values across all model specifications. This indicates that the AR(1) model is the most appropriate choice for capturing the dynamics in our dataset. The consistently lower values across these criteria suggest that the AR(1) model effectively balances model fit with complexity, minimizing the risk of overfitting while providing reliable predictive results. Based on these findings, we are confident that the AR(1) model is not only theoretically sound but also empirically validated as the most suitable model for our analysis. This rigorous model selection process addresses the concerns you raised and ensures that our results are robust and meaningful. We appreciate your constructive feedback and hope this explanation clarifies the rationale behind our model choice. We are committed to maintaining the highest standards of methodological rigor and believe that our approach aligns with best practices in the field. Comment 2: Another point of concern is that the authors only focus on the comparison of the base model with the extended model without showing preliminary results of the univariate regressions. In my opinion, this omission prevents the reader from fully understanding the magnitude and relationship of the estimated beta parameters with the real estate indices. Author Response: Thank you very much for your insightful comment. We understand and appreciate the importance of providing univariate regression analyses to offer a clear and direct view of the relationship between the CPU index and real estate indices. We acknowledge that such analyses can be valuable in understanding the individual impact of the CPU index on each real estate index, particularly in terms of estimating the beta parameters. We believe that univariate regression may not fully capture the complexities involved. Real estate markets often exhibit lagged responses to external factors such as climate policy uncertainty (CPU), meaning that the effects of CPU might not be immediate but could unfold over time. The inherent delay in the market’s response to external shocks is a key characteristic that univariate regression, which does not account for temporal effects, may not adequately address. We added below to guide readers why we chose the methodology. [Line 108 (p.5) ~ Line 111 (p5.)] “We hope this approach addresses your concerns while also maintaining the integrity of the analysis. We deeply appreciate your guidance in refining our work and are open to any further suggestions you might have.” In light of this, we have primarily focused on the AR(1) model and its extended versions in our analysis. These models are designed to incorporate lagged variables, allowing for a more accurate depiction of the temporal dynamics at play. By including lagged terms, the AR(1) model captures the delayed impact of CPU on real estate indices, providing a more nuanced understanding of how these effects evolve over time. This approach better reflects the market’s complexity, where multiple factors interact in a time-dependent manner. Comment 3: In addition, other controls may be included in the extended and base models to capture other dynamics in the considered real estate indices, such as market risk, interest rate risk, and credit risk. Author Response: Thank you for your valuable suggestion. We fully agree that incorporating additional controls, such as market risk, interest rate risk, and credit risk, would provide a more comprehensive understanding of the dynamics influencing real estate indices. We view this as a valuable direction for future research and plan to explore these additional dimensions in subsequent studies. We now emphasize the importance of including these factors in future research to capture the full range of dynamics that impact real estate indices, alongside the broader application of the CPU index across diverse countries and property types. We’ve added below to clearly express your concern; [Line 343 (p.20) ~ Line 344 (p.20)] “Additionally, future studies should consider including other controls, such as market risk, interest rate risk, and credit risk, in both the base and extended models to capture the full range of dynamics influencing real estate indices.” Comment 4: Furthermore, the “Robustness Check” section provides an analysis of the heteroscedasticity and autocorrelation of residuals. Regarding the former, the authors show that, in general, there is no heteroscedasticity, except for the "industrial model," where they use HC3 to adjust standard errors. Regarding autocorrelation, they perform the Durbin-Watson (DW) test, testing only for 1-lag, with no autocorrelation found. Citing the authors: "Results from the DW test for all models approached a value of 2, indicating an absence of serial correlation." However, nothing is done about higher-order autocorrelation. The Breusch-Godfrey procedure can be performed to test for higher-order residual autocorrelation, and the heteroskedasticity and autocorrelation consistent (HAC) covariance estimator should be considered. Additionally, a graphical approach to residuals inspection may help readers better understand the nature of the results. Author Response: (The revised section is [Line 281 p.17 ~ Line 291 p.18]) We appreciate the reviewer’s suggestion to further investigate higher-order autocorrelation. In response, we conducted the Breusch-Godfrey test to assess the presence of higher-order autocorrelation in the residuals of our models. As shown in Table 7, the test results indicate that most models do not exhibit significant higher-order autocorrelation, with p-values exceeding the conventional significance level of 0.05. However, the F.Outlet and Hotel showed potential higher-order autocorrelation, with p-values of 0.002 and 0.054, respectively. To address this issue, we applied the HAC (Heteroscedasticity and Autocorrelation Consistent) covariance estimator to these models. The results revealed that the F.Outlet model had t-statistics and p-value 1.052/0.002, while the Hodel model had values of 0.316/0.054. Notably, these findings are consistent with the out-of-sample R2 result, which also indicated lower explanatory power for these models. Specifically, the lack of statistical significance for the CPU variable in these models suggests that climate policy uncertainty does not significantly influence the volatility of these particular real estate sectors. Table 7 Results of Breuch-Godfrey Test for Higher-Order Autocorrelation LM Statistics p-value AR-CPU-Comp. 1.06 0.78 AR-CPU-Apart. 2.65 0.45 AR-CPU-Ind. 1.6 0.66 AR-CPU-Ret. 2.24 0.523 AR-CPU-Mall. 2.45 0.481 AR-CPU-F.Outlet 14.7 0.002 AR-CPU-Hotel. 7.89 0.054 AR-CPU-Mfchome. 1.95 0.583 AR-CPU-Office. 2.04 0.564 AR-CPU-Resident. 2.41 0.492 We believe that these additional analyses enhance the robustness of our findings and address the concerns raised regarding higher-order autocorrelation. PONE-D-24-24485 - Reviewer 2 Climate Policy Uncertainty and Its Impact on Real Estate Market Dynamics: A Sectoral and Regional Analysis Thank you for another opportunity to revise our manuscript. Our revision again benefitted from your and the editor’s writing guidance. We made a sincere effort to address all of the comments we received and, as a result, have made substantial writing changes to the paper. We hope that these changes and clarifications significantly improve the paper and alleviate the concerns you and the reviewer raised about the previous draft. We look forward to your feedback and would, of course, be happy to make further changes. Below we provide responses to your comments on our paper (reproduced in bold, blue italics). Comment 1: The topic and analysis is robust. Literature review can be enhanced. Examples below. 1.Maurizio d’Amato, Asma Salman, Giulia Mastrodonato and Giampiero Sirleo. (September 2022) Measures of Variability in the Application of Cyclical Capitalization (normal form) of the London Market. Springer Book chapter from the Book Property Valuation and Market Cycle. Book Chapter indexed in Scopus and Clarivate Analytics https://link.springer.com/book/10.1007/978-3-031-09450-7 Author Response: Thank you for your positive feedback on the topic and analysis. We appreciate your suggestion to enhance the literature review by including more recent and relevant sources. Following your recommendation, we have incorporated the work of Maurizio et al. (2022) into our literature review. This addition strengthens our discussion on the cyclical nature of the real estate market and its implications for investment strategies and market dynamics. Specifically, we have revised the literature review to include the following passage: [Line 38 (p.3) ~ Line 43 (p.3)] "While the real estate market is influenced by a variety of factors, including economic cycles, market dynamics, and regulatory changes, the impact of climate policy uncertainty introduces an additional layer of complexity. The real estate market is already known for its cyclical nature, with fluctuations in property values, transaction volumes, and investment strategies that are driven by both intrinsic factors (such as supply and demand dynamics) and extrinsic factors (such as broader economic trends and policy changes) (Phyrr et al., 1990; Roulac, 2020). These cycles can create significant risks for investors, particularly when combined with the uncertainties introduced by evolving climate policies." We believe that this enhancement provides a more comprehensive background for our study and aligns well with your suggestions. Thank you once again for your valuable input, which has helped to improve the overall quality and depth of our literature review. Comment 2: The conclusion should have a policy recommendation. Author Response: We think this is an excellent suggestion. We have added a policy recommendation. [Line 333 (p.19) ~ Line 338 (p.20)] “In light of the findings from this study, policymakers should consider integrating the CPU index into the regulatory framework for real estate markets. This could involve establishing guidelines that require real estate developers and investors to account for CPU-related risks in their financial and operational planning. Additionally, policies aimed at mitigating the impact of CPU, such as incentives for energy efficiency upgrades in vulnerable property types like industrial assets, should be prioritized. By doing so, policymakers can help reduce the volatility induced by CPU and promote more stable and sustainable real estate markets, particularly in regions that are most susceptible to climate change.” Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0311688.r002
24 Sep 2024 Decision Letter - Marco Maria Sorge, Editor Climate Policy Uncertainty and Its Impact on Real Estate Market Dynamics: A Sectoral and Regional Analysis PONE-D-24-24485R1 Dear Dr. Jang, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Marco Maria Sorge, PhD Academic Editor PLOS ONE Additional Editor Comments (optional): Please proof-read the manuscript to improve overall readability, and double-check the references to make sure citations are appropriately reported, e.g. Maurizio et al. (2022) should be d’Amato, M., Salman, A., Sirleo, G. (2022). Measures of Variability in the Application of Cyclical Capitalization (Normal Form) to London Office Market. In: d'Amato, M., Coskun, Y. (eds) Property Valuation and Market Cycle. Springer, Cham. https://doi.org/10.1007/978-3-031-09450-7_15 Reviewers' comments: https://doi.org/10.1371/journal.pone.0311688.r003
Formally Accepted
16 Oct 2024 Acceptance Letter - Marco Maria Sorge, Editor PONE-D-24-24485R1 PLOS ONE Dear Dr. Jang, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Professor Marco Maria Sorge Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0311688.r004

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