I show how financial intermediaries affect rental housing quality and affordability by supplying real estate investors with financing for quality improvement projects (i.e., renovations). First, I document a historic surge in improvement activity since the Great Recession. Then, using exogenous variation generated by a 2015 change in regulatory capital requirements, I find that a reallocation of bank credit toward improvement projects accounts for 24% of quality improvements since 2015. The shock increases the supply of high-quality apartments and lowers their rent. However, it raises the average apartment's rent and accounts for 32% of historically high rent growth over 2015-16.
{"title":"Finance and the Supply of Housing Quality","authors":"Michael Reher","doi":"10.2139/ssrn.3446411","DOIUrl":"https://doi.org/10.2139/ssrn.3446411","url":null,"abstract":"I show how financial intermediaries affect rental housing quality and affordability by supplying real estate investors with financing for quality improvement projects (i.e., renovations). First, I document a historic surge in improvement activity since the Great Recession. Then, using exogenous variation generated by a 2015 change in regulatory capital requirements, I find that a reallocation of bank credit toward improvement projects accounts for 24% of quality improvements since 2015. The shock increases the supply of high-quality apartments and lowers their rent. However, it raises the average apartment's rent and accounts for 32% of historically high rent growth over 2015-16.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74566916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper exploits the sudden mass arrival of refugees to Germany in 2015 to study potential price penalties suffered by residential property in vicinity of refugee reception centers (RRCs). Using novel data on exact locations of publicly-run RRCs in 2014 and 2015 and monthly offers of single-family homes for sale from Germany’s leading online property broker ImmobilienScout24, we find strong evidence in spatial DiD regressions for a sizeable negative effect on house price growth in proximity to such sites. Detached and semi-detached houses located within a 15-minute walking distance of RRCs exhibit, on average, 13 percentage points lower price growth than comparable dwellings beyond this threshold. We corroborate our finding in a battery of robustness tests and additional explorations, including sample restrictions that consider exclusively property on offer for sale within 40 minutes walking distance to RRCs and exogenous variation in the exposure to such sites. ‘Not in my backyard’ (NIMBY) stances among the resident population may explain our finding.
{"title":"\"Not in My Backyard!\" the 2015 Refugee Crisis in Germany","authors":"Kathleen Kürschner Rauck","doi":"10.2139/ssrn.3697189","DOIUrl":"https://doi.org/10.2139/ssrn.3697189","url":null,"abstract":"This paper exploits the sudden mass arrival of refugees to Germany in 2015 to study potential price penalties suffered by residential property in vicinity of refugee reception centers (RRCs). Using novel data on exact locations of publicly-run RRCs in 2014 and 2015 and monthly offers of single-family homes for sale from Germany’s leading online property broker ImmobilienScout24, we find strong evidence in spatial DiD regressions for a sizeable negative effect on house price growth in proximity to such sites. Detached and semi-detached houses located within a 15-minute walking distance of RRCs exhibit, on average, 13 percentage points lower price growth than comparable dwellings beyond this threshold. We corroborate our finding in a battery of robustness tests and additional explorations, including sample restrictions that consider exclusively property on offer for sale within 40 minutes walking distance to RRCs and exogenous variation in the exposure to such sites. ‘Not in my backyard’ (NIMBY) stances among the resident population may explain our finding.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81150740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We investigate the role and impact of household debt on the economic performance of the European economy during the double-dip recession of 2008-2013. We use a loan-level data set of millions of residential mortgages originated between 2000 and 2013 to calculate regional indicators of household debt and property prices. The detailed information allows us to construct a measure of interest rate mis-pricing during the housing boom that we use to identify the effect of a credit shock on household debt. Our analysis provides three main conclusions. First, in the period 2004-2006 the measure of credit shock was negative in most European regions which indicates that credit conditions were significantly relaxed relative to earlier years. Second, we find that regions in which household leverage increased more rapidly during the 2004-2006 period experienced a more severe decline in output and employment after 2008. These results are consistent with the view that an aggregate credit supply shock in Europe boosted household leverage and house prices. Third, we find that the credit shock had the largest effect on increasing leverage for the low-income and the middle-income households, although the change in leverage of the middle-income households represents a more powerful predictor of the decline in economic activity.
{"title":"Household Debt and Economic Growth in Europe","authors":"Luca Barbaglia, S. Manzan, Elisa Tosetti","doi":"10.2139/ssrn.3684399","DOIUrl":"https://doi.org/10.2139/ssrn.3684399","url":null,"abstract":"We investigate the role and impact of household debt on the economic performance of the European economy during the double-dip recession of 2008-2013. We use a loan-level data set of millions of residential mortgages originated between 2000 and 2013 to calculate regional indicators of household debt and property prices. The detailed information allows us to construct a measure of interest rate mis-pricing during the housing boom that we use to identify the effect of a credit shock on household debt. Our analysis provides three main conclusions. First, in the period 2004-2006 the measure of credit shock was negative in most European regions which indicates that credit conditions were significantly relaxed relative to earlier years. Second, we find that regions in which household leverage increased more rapidly during the 2004-2006 period experienced a more severe decline in output and employment after 2008. These results are consistent with the view that an aggregate credit supply shock in Europe boosted household leverage and house prices. Third, we find that the credit shock had the largest effect on increasing leverage for the low-income and the middle-income households, although the change in leverage of the middle-income households represents a more powerful predictor of the decline in economic activity.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"141 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73452760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
China has experienced a rapid boom in real estate prices in the last few decades, leading toa substantial increase in living costs and heavy financial burdens on households. Usingan instrumental variable approach, this paper exploits spatial and temporal variation inhousing price appreciation linked to individual-level health data in China from 2000 to 2011.We find robust evidence that increases in housing prices significantly raise the probability ofresidents having chronic diseases. This negative health impact is more pronounced amongindividuals from low-income families, households that purchased rather than inheritedor was allocated the home, and those who migrated from rural to urban areas. We alsofind evidence that marriage market competition exacerbates these negative health effects,particularly for males and parents with young adult sons. Further empirical results suggestthat housing price appreciation induces negative health consequences through increasedwork intensity, higher mental stress, and reduced sleep time. This paper provides a novelexplanation to the increased prevalence of chronic diseases in China.
{"title":"The Health Consequence of Rising Housing Prices in China","authors":"Y. Xu, Feicheng Wang","doi":"10.2139/ssrn.3686433","DOIUrl":"https://doi.org/10.2139/ssrn.3686433","url":null,"abstract":"China has experienced a rapid boom in real estate prices in the last few decades, leading toa substantial increase in living costs and heavy financial burdens on households. Usingan instrumental variable approach, this paper exploits spatial and temporal variation inhousing price appreciation linked to individual-level health data in China from 2000 to 2011.We find robust evidence that increases in housing prices significantly raise the probability ofresidents having chronic diseases. This negative health impact is more pronounced amongindividuals from low-income families, households that purchased rather than inheritedor was allocated the home, and those who migrated from rural to urban areas. We alsofind evidence that marriage market competition exacerbates these negative health effects,particularly for males and parents with young adult sons. Further empirical results suggestthat housing price appreciation induces negative health consequences through increasedwork intensity, higher mental stress, and reduced sleep time. This paper provides a novelexplanation to the increased prevalence of chronic diseases in China.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"95 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85471518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tomaso Duso, C. Michelsen, Maximilian Schäfer, Kevin Ducbao Tran
Cities worldwide have regulated peer-to-peer short-term rental platforms claiming that those platforms remove apartments from the long-term housing market, causing an in- crease in rents. Establishing and quantifying such a causal link is, however, challenging. We investigate two policy changes in Berlin to first assess how effective they were in regulating Airbnb, the largest online peer-to-peer short-term rental platform. We document that the policy changes reduced the number of entire homes listed on Airbnb substantially, by eight to ten listings per square kilometer. In particular the introduction of limitations on the misuse of regular rental apartments as short-term accommodations, also strongly reduced the average number of days per year that Airbnb listings are available for booking. In a second step, we then use this policy-induced change in Airbnb supply to assess the impact of Airbnb on rents in the city. Our results suggest that each nearby apartment on Airbnb increases average monthly rents by at least seven cents per square meter. This effect is larger for Airbnb listings that are available for rent for a larger part of the year. Further analyses suggest some effect heterogeneity across the city. In particular, areas with lower Airbnb density tend to be affected more by additional Airbnb listings.
{"title":"Airbnb and Rents: Evidence from Berlin","authors":"Tomaso Duso, C. Michelsen, Maximilian Schäfer, Kevin Ducbao Tran","doi":"10.2139/ssrn.3676909","DOIUrl":"https://doi.org/10.2139/ssrn.3676909","url":null,"abstract":"Cities worldwide have regulated peer-to-peer short-term rental platforms claiming that those platforms remove apartments from the long-term housing market, causing an in- crease in rents. Establishing and quantifying such a causal link is, however, challenging. We investigate two policy changes in Berlin to first assess how effective they were in regulating Airbnb, the largest online peer-to-peer short-term rental platform. We document that the policy changes reduced the number of entire homes listed on Airbnb substantially, by eight to ten listings per square kilometer. In particular the introduction of limitations on the misuse of regular rental apartments as short-term accommodations, also strongly reduced the average number of days per year that Airbnb listings are available for booking. In a second step, we then use this policy-induced change in Airbnb supply to assess the impact of Airbnb on rents in the city. Our results suggest that each nearby apartment on Airbnb increases average monthly rents by at least seven cents per square meter. This effect is larger for Airbnb listings that are available for rent for a larger part of the year. Further analyses suggest some effect heterogeneity across the city. In particular, areas with lower Airbnb density tend to be affected more by additional Airbnb listings.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"88 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79119151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The slope of the yield curve has long been found to be a useful predictor of future economic activity, but the relationship is unstable. One change we have identified in this paper is that, starting from the 1990s, movements at the long end of the yield curve have an increase in predictive power. We use a medium-scale DSGE model with a housing sector and a yield curve as a guide to find out the sources of such change. The model implies that an increase in the short-term interest rate and a decrease in the long-term interest rate have different impacts on the economy, and to use the slope as a predictor one needs to distinguish movements at the two ends of the yield curve. Based on simulated data from the model, we find that nominal wage rigidities and the capital adjustment costs are closely related to the predictive power of the yield curve. This result is further confirmed with actual data.
{"title":"Yield Curve and the Macroeconomy: Evidence from a DSGE Model with Housing","authors":"Xiaojin Sun, K. Tsang","doi":"10.2139/ssrn.3659679","DOIUrl":"https://doi.org/10.2139/ssrn.3659679","url":null,"abstract":"The slope of the yield curve has long been found to be a useful predictor of future economic activity, but the relationship is unstable. One change we have identified in this paper is that, starting from the 1990s, movements at the long end of the yield curve have an increase in predictive power. We use a medium-scale DSGE model with a housing sector and a yield curve as a guide to find out the sources of such change. The model implies that an increase in the short-term interest rate and a decrease in the long-term interest rate have different impacts on the economy, and to use the slope as a predictor one needs to distinguish movements at the two ends of the yield curve. Based on simulated data from the model, we find that nominal wage rigidities and the capital adjustment costs are closely related to the predictive power of the yield curve. This result is further confirmed with actual data.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73725306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Mammadova, Arzu Haydarova, A. Malikova, H. Aliyev, O. Mammadov
Being argumentative in nature and referring to Oaxaca Decomposition for the purpose of defining the main drivers of rental flats and houses, be new, old, repaired, or unrepaired, together with applying the difference in difference method to evaluate the effectiveness of the policy, this paper calls into the question of how to inaugurate a country-specific two-sided matching algorithm for rental house allocation based on the empirical results. Model 3 is built on time series data to evaluate the policy implementation by the Azerbaijani government to provide households with financial aid. Based on Blinder-Oaxaca Decomposition, the main findings of the study manifest that for the repaired and unrepaired houses, the price discrimination is mainly explained by the room number while this is an area of the houses per meter square that explains the price gap between old and new flats. The Difference in Difference model signifies that the increase in the number of mortgage loans from 50.000 AZN to 150.000 AZN declined demand more than the increase in supply. Additionally, the study offers 2 Matching Algorithm and Mechanism Design for the allocation of rental houses with existing tenants and newcomers in addition to tenants and owners without initial endowments through YRMH-IGYT in two-sided matching markets.
{"title":"Azerbaijan Housing Market at the Harmony of Blinder-Oaxaca Decomposition and Mechanism Design","authors":"N. Mammadova, Arzu Haydarova, A. Malikova, H. Aliyev, O. Mammadov","doi":"10.2139/ssrn.3796007","DOIUrl":"https://doi.org/10.2139/ssrn.3796007","url":null,"abstract":"Being argumentative in nature and referring to Oaxaca Decomposition for the purpose of defining the main drivers of rental flats and houses, be new, old, repaired, or unrepaired, together with applying the difference in difference method to evaluate the effectiveness of the policy, this paper calls into the question of how to inaugurate a country-specific two-sided matching algorithm for rental house allocation based on the empirical results. Model 3 is built on time series data to evaluate the policy implementation by the Azerbaijani government to provide households with financial aid. Based on Blinder-Oaxaca Decomposition, the main findings of the study manifest that for the repaired and unrepaired houses, the price discrimination is mainly explained by the room number while this is an area of the houses per meter square that explains the price gap between old and new flats. The Difference in Difference model signifies that the increase in the number of mortgage loans from 50.000 AZN to 150.000 AZN declined demand more than the increase in supply. Additionally, the study offers 2 Matching Algorithm and Mechanism Design for the allocation of rental houses with existing tenants and newcomers in addition to tenants and owners without initial endowments through YRMH-IGYT in two-sided matching markets.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85530120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiangyu Feng, Nir Jaimovich, Krishna Rao, S. Terry, Nicolas Vincent
Exploiting data on tens of millions of housing transactions, we show that (1) house prices grew by less in manufacturing-heavy US regions, (2) that this pattern is especially present for the lowest-value homes, and (3) that price declines coincided with worse labor market outcomes, consistent with an income channel. Counterfactual accounting exercises reveal that regional differences in the growth of these lowest-value homes are an important driver of the changes in overall house price inequality. Hence, the economic decline in manufacturing-heavy areas extends far beyond income and employment flows to house prices.
{"title":"Location, Location, Location: Manufacturing and House Price Growth","authors":"Xiangyu Feng, Nir Jaimovich, Krishna Rao, S. Terry, Nicolas Vincent","doi":"10.2139/ssrn.3607876","DOIUrl":"https://doi.org/10.2139/ssrn.3607876","url":null,"abstract":"\u0000 Exploiting data on tens of millions of housing transactions, we show that (1) house prices grew by less in manufacturing-heavy US regions, (2) that this pattern is especially present for the lowest-value homes, and (3) that price declines coincided with worse labor market outcomes, consistent with an income channel. Counterfactual accounting exercises reveal that regional differences in the growth of these lowest-value homes are an important driver of the changes in overall house price inequality. Hence, the economic decline in manufacturing-heavy areas extends far beyond income and employment flows to house prices.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86688016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper develops an artificial intelligence-based automated valuation model (AI-AVM) using the decision tree and the boosting techniques to predict residential property prices in Singapore. We use more than 300,000 property transaction data from Singapore’s private residential property market for the period from 1995 to 2017 for the training of the AI-AVM models. The two tree-based AI-AVM models show superior performance over the traditional multiple regression analysis (MRA) model when predicting the property prices. We also extend the application of the AI-AVM to more homogenous public housing prices in Singapore, and the predictive performance remains robust. The boosting AI-AVM models that allow for inter-dependence structure in the decision trees is the best model that explains more than 88% of the variance in both private and public housing prices and keep the prediction errors to less than 6% for HDB and 9% for the private market. When subject the AI-AVM to the out-of-sample forecasting using the 2017-2019 testing property sale samples, the prediction errors remain within a narrow range of between 5% and 9%.
{"title":"Decision Tree and Boosting Techniques in Artificial Intelligence Based Automated Valuation Models (AI-AVM)","authors":"T. Sing, J. Yang, S. Yu","doi":"10.2139/ssrn.3605798","DOIUrl":"https://doi.org/10.2139/ssrn.3605798","url":null,"abstract":"This paper develops an artificial intelligence-based automated valuation model (AI-AVM) using the decision tree and the boosting techniques to predict residential property prices in Singapore. We use more than 300,000 property transaction data from Singapore’s private residential property market for the period from 1995 to 2017 for the training of the AI-AVM models. The two tree-based AI-AVM models show superior performance over the traditional multiple regression analysis (MRA) model when predicting the property prices. We also extend the application of the AI-AVM to more homogenous public housing prices in Singapore, and the predictive performance remains robust. The boosting AI-AVM models that allow for inter-dependence structure in the decision trees is the best model that explains more than 88% of the variance in both private and public housing prices and keep the prediction errors to less than 6% for HDB and 9% for the private market. When subject the AI-AVM to the out-of-sample forecasting using the 2017-2019 testing property sale samples, the prediction errors remain within a narrow range of between 5% and 9%.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91072306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is important to manage the production by analyzing the demand in the market. The market faces uncertain demands, short life cycle and lack of historical sales data due to which, forecasting becomes challenging. Various approaches have been proposed over the past few decades concerning this issue. This paper forms a basis for understanding the prediction mechanism by presenting a comprehensive literature review along with various domains in which sales forecasting can be done. For any prediction process we can’t define a certain model that will just outperform every other model but we can try and find suitable model according to the data. We have defined various models and also a hybrid model to predict the selling price of house based on its features, thus feature engineering is also applied to extract a fruitful dataset.
{"title":"House Price Forecasting Based on Hybrid Multi-regression Model","authors":"Shivdutt Vishwakarma, Swasti Singhal","doi":"10.2139/ssrn.3601507","DOIUrl":"https://doi.org/10.2139/ssrn.3601507","url":null,"abstract":"It is important to manage the production by analyzing the demand in the market. The market faces uncertain demands, short life cycle and lack of historical sales data due to which, forecasting becomes challenging. Various approaches have been proposed over the past few decades concerning this issue. This paper forms a basis for understanding the prediction mechanism by presenting a comprehensive literature review along with various domains in which sales forecasting can be done. For any prediction process we can’t define a certain model that will just outperform every other model but we can try and find suitable model according to the data. We have defined various models and also a hybrid model to predict the selling price of house based on its features, thus feature engineering is also applied to extract a fruitful dataset.","PeriodicalId":12014,"journal":{"name":"ERN: Microeconometric Studies of Housing Markets (Topic)","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78577958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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