Pub Date : 2022-11-16DOI: 10.1108/ijhma-09-2022-0143
Ahmet Gokce Akpolat
Purpose This study aims to examine the impact of some real variables such as real effective exchange rates, real mortgage rates, real money supply, real construction cost index and housing sales on the real housing prices. Design/methodology/approach This study uses a nonlinear autoregressive distributed lag (NARDL) model in the monthly period of 2010:1–2021:10. Findings The real effective exchange rate has a positive and symmetric effect. The decreasing effect of negative changes in real money supply on real housing prices is higher than the increasing effect of positive changes. Only positive changes in the real construction cost index have an increasing and statistically significant effect on real house prices, while only negative changes in housing sales have a small negative sign and a small increasing effect on housing prices. The fact that the positive and negative changes in real mortgage rates are negative and positive, respectively, indicates that both have a reducing effect on real housing prices. Originality/value This study suggests the first NARDL model that investigates the asymmetric effects on real housing prices instead of nominal housing prices for Turkey. In addition, the study is the first, to the best of the authors’ knowledge, to examine the effects of the five real variables on real housing prices.
{"title":"The asymmetric effects of real variables on real housing prices: a nonlinear ARDL analysis for Turkey","authors":"Ahmet Gokce Akpolat","doi":"10.1108/ijhma-09-2022-0143","DOIUrl":"https://doi.org/10.1108/ijhma-09-2022-0143","url":null,"abstract":"\u0000Purpose\u0000This study aims to examine the impact of some real variables such as real effective exchange rates, real mortgage rates, real money supply, real construction cost index and housing sales on the real housing prices.\u0000\u0000\u0000Design/methodology/approach\u0000This study uses a nonlinear autoregressive distributed lag (NARDL) model in the monthly period of 2010:1–2021:10.\u0000\u0000\u0000Findings\u0000The real effective exchange rate has a positive and symmetric effect. The decreasing effect of negative changes in real money supply on real housing prices is higher than the increasing effect of positive changes. Only positive changes in the real construction cost index have an increasing and statistically significant effect on real house prices, while only negative changes in housing sales have a small negative sign and a small increasing effect on housing prices. The fact that the positive and negative changes in real mortgage rates are negative and positive, respectively, indicates that both have a reducing effect on real housing prices.\u0000\u0000\u0000Originality/value\u0000This study suggests the first NARDL model that investigates the asymmetric effects on real housing prices instead of nominal housing prices for Turkey. In addition, the study is the first, to the best of the authors’ knowledge, to examine the effects of the five real variables on real housing prices.\u0000","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46448274","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}
Purpose The purpose of this study is to identify the situation of spatial inequality in the residential system of Tehran city in terms of housing prices in the year 2021 and to examine its changes over time (1991–2021). Design/methodology/approach In terms of purpose, this study is applied research and has used a descriptive-analytical method. The statistical population of this research is the residential units in Tehran city 2021. The average per square meter of a residential unit in the level of city neighborhoods was entered in the geographical information system (GIS) in 2021. Moran’s spatial autocorrelation method, map cluster analysis (hot and cold spots) and Kriging interpolation have been used for spatial analysis of points. Then, the change in spatial inequality in the residential system of Tehran city has been studied and measured based on the price per square meter of a residential unit for 30 years in the 22 districts of Tehran by using statistical clustering based on distance with standard deviation. Findings The result of spatial autocorrelation analysis with a score of 0.873872 and a p-value equal to 0.000000 indicates a cluster distribution of housing prices throughout the city. The results of hot spots show that the highest concentration of hot spots (the highest price) is in the northern part of the city, and the highest concentration of cold spots (the lowest price) is in the southern part of Tehran city. Calculating the area and estimating the quantitative values of data-free points by the use of the Kriging interpolation method indicates that 9.95% of Tehran’s area has a price of less than US$800, 17.68% of it has a price of US$800 to US$1,200, 25.40% has the price of US$1,200 to US$1,600, 17.61% has the price of US$1,600 to US$2,000, 9.54% has the price of US$2,000 to US$2,200, 6.69% has the price of US$2,200 to US$2,600, 5.38% has the price of US$2,600 to US$2,800, 4.59% has the price of US$2,800 to US$3,200 and finally, the 3.16% has a price more than US$3,200. The highest price concentration (above US$3,200) is in five neighborhoods (Zafaranieh, Mahmoudieh, Tajrish, Bagh-Ferdows and Hesar Bou-Ali). The findings from the study of changes in housing prices in the period (1991–2021) indicate that the southern part of Tehran has grown slightly compared to the average range, and the western part of Tehran, which includes the 21st and 22nd regions with much more growth than the average price. Originality/value There is massive inequality in housing prices in different areas and neighborhoods of Tehran city in 2021. In the period under study, spatial inequality in the residential system of Tehran intensified. The considerable increase in housing prices in the housing market of Tehran has made this sector a commodity, intensifying the inequality between owners and non-owners. This increase in housing price inequality has caused an increase in the informal living for the population of the southern part. This population is e
{"title":"Spatial analysis of housing prices in Tehran city","authors":"Seyedeh Mehrangar Hosseini, Behnaz Bahadori, Shahram Charkhan","doi":"10.1108/ijhma-06-2022-0087","DOIUrl":"https://doi.org/10.1108/ijhma-06-2022-0087","url":null,"abstract":"\u0000Purpose\u0000The purpose of this study is to identify the situation of spatial inequality in the residential system of Tehran city in terms of housing prices in the year 2021 and to examine its changes over time (1991–2021).\u0000\u0000\u0000Design/methodology/approach\u0000In terms of purpose, this study is applied research and has used a descriptive-analytical method. The statistical population of this research is the residential units in Tehran city 2021. The average per square meter of a residential unit in the level of city neighborhoods was entered in the geographical information system (GIS) in 2021. Moran’s spatial autocorrelation method, map cluster analysis (hot and cold spots) and Kriging interpolation have been used for spatial analysis of points. Then, the change in spatial inequality in the residential system of Tehran city has been studied and measured based on the price per square meter of a residential unit for 30 years in the 22 districts of Tehran by using statistical clustering based on distance with standard deviation.\u0000\u0000\u0000Findings\u0000The result of spatial autocorrelation analysis with a score of 0.873872 and a p-value equal to 0.000000 indicates a cluster distribution of housing prices throughout the city. The results of hot spots show that the highest concentration of hot spots (the highest price) is in the northern part of the city, and the highest concentration of cold spots (the lowest price) is in the southern part of Tehran city. Calculating the area and estimating the quantitative values of data-free points by the use of the Kriging interpolation method indicates that 9.95% of Tehran’s area has a price of less than US$800, 17.68% of it has a price of US$800 to US$1,200, 25.40% has the price of US$1,200 to US$1,600, 17.61% has the price of US$1,600 to US$2,000, 9.54% has the price of US$2,000 to US$2,200, 6.69% has the price of US$2,200 to US$2,600, 5.38% has the price of US$2,600 to US$2,800, 4.59% has the price of US$2,800 to US$3,200 and finally, the 3.16% has a price more than US$3,200. The highest price concentration (above US$3,200) is in five neighborhoods (Zafaranieh, Mahmoudieh, Tajrish, Bagh-Ferdows and Hesar Bou-Ali). The findings from the study of changes in housing prices in the period (1991–2021) indicate that the southern part of Tehran has grown slightly compared to the average range, and the western part of Tehran, which includes the 21st and 22nd regions with much more growth than the average price.\u0000\u0000\u0000Originality/value\u0000There is massive inequality in housing prices in different areas and neighborhoods of Tehran city in 2021. In the period under study, spatial inequality in the residential system of Tehran intensified. The considerable increase in housing prices in the housing market of Tehran has made this sector a commodity, intensifying the inequality between owners and non-owners. This increase in housing price inequality has caused an increase in the informal living for the population of the southern part. This population is e","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49056022","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}
Pub Date : 2022-10-21DOI: 10.1108/ijhma-06-2022-0089
W. He, Shaomeng Jia
Purpose This paper aims to investigate the increasing trend of multigenerational co-living in the USA and to research the socioeconomic and cultural determinants of such decision. Design/methodology/approach This study uses the 2017 American Housing Survey data to run descriptive and regression analysis. Findings The authors find household income appears consistently to be the most significant factor determining multigenerational co-residence decision across all household compositions. Latino households are most likely to co-reside with multiple generations, followed by Asian and African American households. Immigrants tend to live in multigenerational co-residential housing units with smaller sizes and more impoverished neighborhoods, but show greater flexibility in making residential arrangements once they gain better education. In addition, older householders or female householders are significantly more likely to co-reside with multiple generations. Living in metropolitan areas has no impact on co-residence choice, although some evidence suggests that multigenerational co-residential families tend to live in inferior neighborhoods. Research limitations/implications This study provides updated evidence on multigenerational co-residence choice in the contemporary United States. The findings provide evidence on how households make residential choices in response to financial hardships and contribute to the theoretical understanding of the variations of such decisions among immigrants and different ethnic and aging groups. Practical implications This study on multigenerational co-residence choice imposes important practical implications. The unprecedented COVID-19 pandemic creates ideal research setting to study how households cope with the tremendous uncertainties in the job markets and financial markets. Although multigenerational co-living may work well for some households with lower or moderate-income for financial reasons, it is not an attractive option for every family. Social implications Sharing a home with multiple generations can be challenging. Policymakers should design policies and programs to provide households with guidance on how to live peacefully in multigenerational settings and make multigenerational co-living an appealing and cost-effective housing option for American families of all means. Originality/value This study contributes to the existing literature by providing new evidence on the determinants of multigenerational co-residence decision. This study’s findings are fundamental to guide policymakers in carrying out policies and programs aimed at providing a more appealing and cost-effective housing arrangement for American families. The evidence on the senior and minority subsamples are especially meaningful as the vast majority of the baby boom generation in the USA is aging and substantial growth is expected in multigenerational households over the next several decades. Understanding the increasing burden
{"title":"Exploring multigenerational co-residence in the United States","authors":"W. He, Shaomeng Jia","doi":"10.1108/ijhma-06-2022-0089","DOIUrl":"https://doi.org/10.1108/ijhma-06-2022-0089","url":null,"abstract":"\u0000Purpose\u0000This paper aims to investigate the increasing trend of multigenerational co-living in the USA and to research the socioeconomic and cultural determinants of such decision.\u0000\u0000\u0000Design/methodology/approach\u0000This study uses the 2017 American Housing Survey data to run descriptive and regression analysis.\u0000\u0000\u0000Findings\u0000The authors find household income appears consistently to be the most significant factor determining multigenerational co-residence decision across all household compositions. Latino households are most likely to co-reside with multiple generations, followed by Asian and African American households. Immigrants tend to live in multigenerational co-residential housing units with smaller sizes and more impoverished neighborhoods, but show greater flexibility in making residential arrangements once they gain better education. In addition, older householders or female householders are significantly more likely to co-reside with multiple generations. Living in metropolitan areas has no impact on co-residence choice, although some evidence suggests that multigenerational co-residential families tend to live in inferior neighborhoods.\u0000\u0000\u0000Research limitations/implications\u0000This study provides updated evidence on multigenerational co-residence choice in the contemporary United States. The findings provide evidence on how households make residential choices in response to financial hardships and contribute to the theoretical understanding of the variations of such decisions among immigrants and different ethnic and aging groups.\u0000\u0000\u0000Practical implications\u0000This study on multigenerational co-residence choice imposes important practical implications. The unprecedented COVID-19 pandemic creates ideal research setting to study how households cope with the tremendous uncertainties in the job markets and financial markets. Although multigenerational co-living may work well for some households with lower or moderate-income for financial reasons, it is not an attractive option for every family.\u0000\u0000\u0000Social implications\u0000Sharing a home with multiple generations can be challenging. Policymakers should design policies and programs to provide households with guidance on how to live peacefully in multigenerational settings and make multigenerational co-living an appealing and cost-effective housing option for American families of all means.\u0000\u0000\u0000Originality/value\u0000This study contributes to the existing literature by providing new evidence on the determinants of multigenerational co-residence decision. This study’s findings are fundamental to guide policymakers in carrying out policies and programs aimed at providing a more appealing and cost-effective housing arrangement for American families. The evidence on the senior and minority subsamples are especially meaningful as the vast majority of the baby boom generation in the USA is aging and substantial growth is expected in multigenerational households over the next several decades. Understanding the increasing burden ","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48717075","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}
Pub Date : 2022-10-10DOI: 10.1108/ijhma-08-2022-0107
Kurt Wurthmann
Purpose This study aims to provide and illustrate the application of a framework for conducting techno-economic analyses (TEA) of early-stage designs for net-zero water and energy, single-family homes that meet affordable housing criteria in diverse locations. Design/methodology/approach The framework is developed and applied in a case example of a TEA of four designs for achieving net zero-water and energy in an affordable home in Saint Lucie County, Florida. Findings Homes built and sold at current market prices, using combinations of well versus rainwater harvesting (RWH) systems and grid-tied versus hybrid solar photovoltaic (PV) systems, can meet affordable housing criteria for moderate-income families, when 30-year fixed-rate mortgages are at 2%–3%. As rates rise to 6%, unless battery costs drop by 40% and 60%, respectively, homes using hybrid solar PV systems combined with well versus RWH systems cease to meet affordable housing criteria. For studied water and electricity usage and 6% interest rates, only well and grid-tied solar PV systems provide water and electricity at costs below current public supply prices. Originality/value This article provides a highly adaptable framework for conducting TEAs in diverse locations for designs of individual net-zero water and energy affordable homes and whole subdivisions of such homes. The framework includes a new technique for sizing storage tanks for residential RWH systems and provides a foundation for future research at the intersection of affordable housing development and residential net-zero water and energy systems design.
{"title":"Conducting techno-economic analyses of early-stage designs for net-zero water and energy affordable homes","authors":"Kurt Wurthmann","doi":"10.1108/ijhma-08-2022-0107","DOIUrl":"https://doi.org/10.1108/ijhma-08-2022-0107","url":null,"abstract":"\u0000Purpose\u0000This study aims to provide and illustrate the application of a framework for conducting techno-economic analyses (TEA) of early-stage designs for net-zero water and energy, single-family homes that meet affordable housing criteria in diverse locations.\u0000\u0000\u0000Design/methodology/approach\u0000The framework is developed and applied in a case example of a TEA of four designs for achieving net zero-water and energy in an affordable home in Saint Lucie County, Florida.\u0000\u0000\u0000Findings\u0000Homes built and sold at current market prices, using combinations of well versus rainwater harvesting (RWH) systems and grid-tied versus hybrid solar photovoltaic (PV) systems, can meet affordable housing criteria for moderate-income families, when 30-year fixed-rate mortgages are at 2%–3%. As rates rise to 6%, unless battery costs drop by 40% and 60%, respectively, homes using hybrid solar PV systems combined with well versus RWH systems cease to meet affordable housing criteria. For studied water and electricity usage and 6% interest rates, only well and grid-tied solar PV systems provide water and electricity at costs below current public supply prices.\u0000\u0000\u0000Originality/value\u0000This article provides a highly adaptable framework for conducting TEAs in diverse locations for designs of individual net-zero water and energy affordable homes and whole subdivisions of such homes. The framework includes a new technique for sizing storage tanks for residential RWH systems and provides a foundation for future research at the intersection of affordable housing development and residential net-zero water and energy systems design.\u0000","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46824579","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}
Pub Date : 2022-10-05DOI: 10.1108/ijhma-06-2022-0093
Fredrick Otieno Okuta, T. Kivaa, Raphael M. Kieti, J. O. Okaka
Purpose This paper studies the dynamic effects of selected macroeconomic factors on the performance of the housing market in Kenya using Autoregressive Distributed Lag (ARDL) Models. This study aims to explain the dynamic effects of the macroeconomic factors on the three indicators of the housing market performance: housing prices growth, sales index and rent index. Design/methodology/approach This study used ARDL Models on time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited. Findings The results indicate that household income, gross domestic product (GDP), inflation rates and exchange rates have both short-run and long-run effects on housing prices while interest rates, diaspora remittance, construction output and urban population have no significant effects on housing prices both in the short and long run. However, only household income, interest rates, private capital inflows and exchange rates have a significant effect on housing sales both in the short and long run. Furthermore, household income, GDP, interest rates and exchange rates significantly affect housing rental growth in the short and long run. The findings are key for policymaking, especially at the appraisal stages of real estate investments by the developers. Practical implications The authors recommend the use of both the traditional hedonic models in conjunction with the dynamic models during real estate project appraisals as this would ensure that developers only invest in the right projects in the right economic situations. Originality/value The imbalance between housing demand and supply has prompted an investigation into the role of macroeconomic variables on the housing market in Kenya. Although the effects of the variables have been documented, there is a need to document the short-run and long-term effects of the factors to precisely understand the behavior of the housing market as a way of shielding developers from economic losses.
{"title":"Modeling the dynamic effects of macroeconomic factors on housing performance in Kenya","authors":"Fredrick Otieno Okuta, T. Kivaa, Raphael M. Kieti, J. O. Okaka","doi":"10.1108/ijhma-06-2022-0093","DOIUrl":"https://doi.org/10.1108/ijhma-06-2022-0093","url":null,"abstract":"\u0000Purpose\u0000This paper studies the dynamic effects of selected macroeconomic factors on the performance of the housing market in Kenya using Autoregressive Distributed Lag (ARDL) Models. This study aims to explain the dynamic effects of the macroeconomic factors on the three indicators of the housing market performance: housing prices growth, sales index and rent index.\u0000\u0000\u0000Design/methodology/approach\u0000This study used ARDL Models on time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited.\u0000\u0000\u0000Findings\u0000The results indicate that household income, gross domestic product (GDP), inflation rates and exchange rates have both short-run and long-run effects on housing prices while interest rates, diaspora remittance, construction output and urban population have no significant effects on housing prices both in the short and long run. However, only household income, interest rates, private capital inflows and exchange rates have a significant effect on housing sales both in the short and long run. Furthermore, household income, GDP, interest rates and exchange rates significantly affect housing rental growth in the short and long run. The findings are key for policymaking, especially at the appraisal stages of real estate investments by the developers.\u0000\u0000\u0000Practical implications\u0000The authors recommend the use of both the traditional hedonic models in conjunction with the dynamic models during real estate project appraisals as this would ensure that developers only invest in the right projects in the right economic situations.\u0000\u0000\u0000Originality/value\u0000The imbalance between housing demand and supply has prompted an investigation into the role of macroeconomic variables on the housing market in Kenya. Although the effects of the variables have been documented, there is a need to document the short-run and long-term effects of the factors to precisely understand the behavior of the housing market as a way of shielding developers from economic losses.\u0000","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48567248","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}
Pub Date : 2022-10-04DOI: 10.1108/ijhma-07-2022-0104
Roozbeh Balounejad Nouri
Purpose The purpose of this study, the nonlinear relationship between the real estate market and the stock market was investigated in Iran. For this intent, the monthly data from 2012:4 to 2022:5 is used. Design/methodology/approach In this study, the quantile-on-quantile estimation method is used, which is a combination of the nonparametric estimation methods and the quantile regression. Findings The research results show that, in the low quantiles, the effect of stock market return on the housing market return is negative or zero. In fact, in this situation, the increasing returns in the stock market will shift part of the financial resources of the economy to the market and create stagnation or even negative returns in the housing market. This situation is seen more strongly in some other quantiles, including the 0.25 and 0.75 quantiles; in contrast, the effect of high quantiles of stock market returns is positive on the housing market. Originality/value It seems that the demand in the housing market increase in a situation where the returns of the stock market are growing, and the market is in a bullish condition, and this causes an increase in the price and returns in this market. In addition, the results show that the effect of stock market returns on capital market returns is asymmetric and nonlinear.
{"title":"Investigating the asymmetric relationship between housing prices and the stock market in Iran: quantile-on-quantile approach","authors":"Roozbeh Balounejad Nouri","doi":"10.1108/ijhma-07-2022-0104","DOIUrl":"https://doi.org/10.1108/ijhma-07-2022-0104","url":null,"abstract":"\u0000Purpose\u0000The purpose of this study, the nonlinear relationship between the real estate market and the stock market was investigated in Iran. For this intent, the monthly data from 2012:4 to 2022:5 is used.\u0000\u0000\u0000Design/methodology/approach\u0000In this study, the quantile-on-quantile estimation method is used, which is a combination of the nonparametric estimation methods and the quantile regression.\u0000\u0000\u0000Findings\u0000The research results show that, in the low quantiles, the effect of stock market return on the housing market return is negative or zero. In fact, in this situation, the increasing returns in the stock market will shift part of the financial resources of the economy to the market and create stagnation or even negative returns in the housing market. This situation is seen more strongly in some other quantiles, including the 0.25 and 0.75 quantiles; in contrast, the effect of high quantiles of stock market returns is positive on the housing market.\u0000\u0000\u0000Originality/value\u0000It seems that the demand in the housing market increase in a situation where the returns of the stock market are growing, and the market is in a bullish condition, and this causes an increase in the price and returns in this market. In addition, the results show that the effect of stock market returns on capital market returns is asymmetric and nonlinear.\u0000","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2022-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42427479","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}
Pub Date : 2022-09-30DOI: 10.1108/ijhma-07-2022-0102
Franziska Ploessl, Tobias Just
Purpose To investigate whether additional information of the permanent news flow, especially reporting intensity, can help to increase transparency in housing markets, this study aims to examine the relationship between news coverage or news sentiment and residential real estate prices in Germany at a regional level. Design/methodology/approach Using methods in the field of natural language processing, in particular word embeddings and dictionary-based sentiment analyses, the authors derive five different sentiment measures from almost 320,000 news articles of two professional German real estate news providers. These sentiment indicators are used as covariates in a first difference fixed effects regression to investigate the relationship between news coverage or news sentiment and residential real estate prices. Findings The empirical results suggest that the ascertained news-based indicators have a significant positive relationship with residential real estate prices. It appears that the combination of news coverage and news sentiment proves to be a reliable indicator. Furthermore, the extracted sentiment measures lead residential real estate prices up to two quarters. Finally, the explanatory power increases when regressing on prices for condominiums compared with houses, implying that the indicators may rather reflect investor sentiment. Originality/value To the best of the authors’ knowledge, this is the first paper to extract both the news coverage and news sentiment from real estate-related news for regional German housing markets. The approach presented in this study to quantify additional qualitative data from texts is replicable and can be applied to many further research areas on real estate topics.
{"title":"News coverage vs sentiment: evaluating German residential real estate markets","authors":"Franziska Ploessl, Tobias Just","doi":"10.1108/ijhma-07-2022-0102","DOIUrl":"https://doi.org/10.1108/ijhma-07-2022-0102","url":null,"abstract":"\u0000Purpose\u0000To investigate whether additional information of the permanent news flow, especially reporting intensity, can help to increase transparency in housing markets, this study aims to examine the relationship between news coverage or news sentiment and residential real estate prices in Germany at a regional level.\u0000\u0000\u0000Design/methodology/approach\u0000Using methods in the field of natural language processing, in particular word embeddings and dictionary-based sentiment analyses, the authors derive five different sentiment measures from almost 320,000 news articles of two professional German real estate news providers. These sentiment indicators are used as covariates in a first difference fixed effects regression to investigate the relationship between news coverage or news sentiment and residential real estate prices.\u0000\u0000\u0000Findings\u0000The empirical results suggest that the ascertained news-based indicators have a significant positive relationship with residential real estate prices. It appears that the combination of news coverage and news sentiment proves to be a reliable indicator. Furthermore, the extracted sentiment measures lead residential real estate prices up to two quarters. Finally, the explanatory power increases when regressing on prices for condominiums compared with houses, implying that the indicators may rather reflect investor sentiment.\u0000\u0000\u0000Originality/value\u0000To the best of the authors’ knowledge, this is the first paper to extract both the news coverage and news sentiment from real estate-related news for regional German housing markets. The approach presented in this study to quantify additional qualitative data from texts is replicable and can be applied to many further research areas on real estate topics.\u0000","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42814587","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}
Pub Date : 2022-09-28DOI: 10.1108/ijhma-07-2022-0106
T. Jandásková, T. Hrdlicka, Martin Cupal, Petr Kleparnik, M. Komosná, Marek Kervitcer
Purpose This study aims to provide a framework for assessing the technical condition of a house to determine its market value, including the identification of other price-setting factors and their statistical significance. Time on market (TOM) in relation to the technical condition of a house is also addressed. Design/methodology/approach The primary database contains 631 houses, and the initial asking price and selling price are examined. All the houses are located in the Brno–venkov district in the Czech Republic. Regression analysis was used to test the influence of price-setting factors. The standard ordinary least squares estimator and the maximum likelihood estimator were used in the frame of generalized linear models. Findings Using envelope components of houses separately, such as the façade condition, windows, roof, condition of interior and year of construction, brings better results than using a single factor for the technical condition. TOM was found to be 67 days lower for houses intended for demolition – as compared to new houses – and 18 days lower for houses to refurbishment. Originality/value To the best of the authors’ knowledge, this paper is original in the substitution of specific price-setting factors for factors relating to the technical condition of houses as well as in proposing the framework for professionals in the Czech Republic.
{"title":"Technical condition of houses: a framework for the Czech market","authors":"T. Jandásková, T. Hrdlicka, Martin Cupal, Petr Kleparnik, M. Komosná, Marek Kervitcer","doi":"10.1108/ijhma-07-2022-0106","DOIUrl":"https://doi.org/10.1108/ijhma-07-2022-0106","url":null,"abstract":"\u0000Purpose\u0000This study aims to provide a framework for assessing the technical condition of a house to determine its market value, including the identification of other price-setting factors and their statistical significance. Time on market (TOM) in relation to the technical condition of a house is also addressed.\u0000\u0000\u0000Design/methodology/approach\u0000The primary database contains 631 houses, and the initial asking price and selling price are examined. All the houses are located in the Brno–venkov district in the Czech Republic. Regression analysis was used to test the influence of price-setting factors. The standard ordinary least squares estimator and the maximum likelihood estimator were used in the frame of generalized linear models.\u0000\u0000\u0000Findings\u0000Using envelope components of houses separately, such as the façade condition, windows, roof, condition of interior and year of construction, brings better results than using a single factor for the technical condition. TOM was found to be 67 days lower for houses intended for demolition – as compared to new houses – and 18 days lower for houses to refurbishment.\u0000\u0000\u0000Originality/value\u0000To the best of the authors’ knowledge, this paper is original in the substitution of specific price-setting factors for factors relating to the technical condition of houses as well as in proposing the framework for professionals in the Czech Republic.\u0000","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48332157","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}
Pub Date : 2022-09-22DOI: 10.1108/ijhma-06-2022-0085
Samar Ajeeb, W. S. Lai
Purpose This study attempts to find the response of the real estate market to economic changes by identifying cause-effect relationships between mortgage, residential investment, and Saudi employment. Design/methodology/approach A quantitative approach to analytically examine the relationship among the variables. To find out the impact of investment, mortgage and Saudi employment on the Saudi real estate growth from 1970 to 2019. All data sets were obtained from the General Authority for Statistics (GAST), Saudi Central Bank (SAMA) and World Bank Group. Findings This study reveals a positive relationship between the mortgage and GDP in the Saudi Arabian real estate market. The same results for employment and investment; both have a positive effect on the GDP of the real estate market. Research limitations/implications Analyzing the impact of real estate financing on various industries and the extent to which it is related to employment and unemployment rates is essential for future research. Moreover, this research can be applied to different countries and compared based on similarities and differences in implementing mortgage-related policies. Practical implications The government must encourage investment in various ways and establish a stable structure that ensures market stability and finds a balance between supply and demand. Social implications This study reflects the importance of real estate financing not only to individuals and governments but also to investors and business workers, and it is essential to analyze the impact of real estate financing on various industries, as well as the extent to which it is related to employment and unemployment rates. This research can be applied to different countries and compared based on similarities and differences in the implementation of mortgage-related policies. Originality/value This study contributes to testing this study’s hypothesis: that mortgage positively impacts the real estate market of Saudi Arabia.
{"title":"The impact of the mortgage on the real estate market: a study case in Saudi Arabia","authors":"Samar Ajeeb, W. S. Lai","doi":"10.1108/ijhma-06-2022-0085","DOIUrl":"https://doi.org/10.1108/ijhma-06-2022-0085","url":null,"abstract":"\u0000Purpose\u0000This study attempts to find the response of the real estate market to economic changes by identifying cause-effect relationships between mortgage, residential investment, and Saudi employment.\u0000\u0000\u0000Design/methodology/approach\u0000A quantitative approach to analytically examine the relationship among the variables. To find out the impact of investment, mortgage and Saudi employment on the Saudi real estate growth from 1970 to 2019. All data sets were obtained from the General Authority for Statistics (GAST), Saudi Central Bank (SAMA) and World Bank Group.\u0000\u0000\u0000Findings\u0000This study reveals a positive relationship between the mortgage and GDP in the Saudi Arabian real estate market. The same results for employment and investment; both have a positive effect on the GDP of the real estate market.\u0000\u0000\u0000Research limitations/implications\u0000Analyzing the impact of real estate financing on various industries and the extent to which it is related to employment and unemployment rates is essential for future research. Moreover, this research can be applied to different countries and compared based on similarities and differences in implementing mortgage-related policies.\u0000\u0000\u0000Practical implications\u0000The government must encourage investment in various ways and establish a stable structure that ensures market stability and finds a balance between supply and demand.\u0000\u0000\u0000Social implications\u0000This study reflects the importance of real estate financing not only to individuals and governments but also to investors and business workers, and it is essential to analyze the impact of real estate financing on various industries, as well as the extent to which it is related to employment and unemployment rates. This research can be applied to different countries and compared based on similarities and differences in the implementation of mortgage-related policies.\u0000\u0000\u0000Originality/value\u0000This study contributes to testing this study’s hypothesis: that mortgage positively impacts the real estate market of Saudi Arabia.\u0000","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44991043","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}
Pub Date : 2022-09-22DOI: 10.1108/ijhma-07-2022-0103
Rafiq Ahmed, H. Visas, Jabbar Ul-Haq
Purpose This study aims to explore the impact of oil prices on housing prices using Pakistani annual data from 1973 to 2021. Design/methodology/approach The Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests were used for unit-root testing, whereas the johansen-juselius test was used for cointegration. For the short-run, the error correction model is used and the robustness of the model is checked using the dynamic ordinary least squares (DOLS) and fully modified OLS (FMOLS). The cumulative sum (CUSUM) and CUSUM of Squares tests were used to check the stability of the model, while parameter instability was confirmed by the Chow breakpoint test. Finally, the impulse response function was used for causality. Findings According to the findings, rising oil prices, among other things, have an impact on housing prices. Inflation is the single most important factor affecting not only the housing sector but also the entire economy. Lending and exchange rates have a significant impact on housing prices as well. The FMOLS and DOLS results suggest that the OLS results are robust. According to the variance decomposition model, housing prices and oil prices are bidirectionally related. The Government of Pakistan must develop a housing policy on a regular basis to develop the country’s urban housing supply and demand. Practical implications It is suggested that in Pakistan, the rising oil prices is a problem for the housing prices as well as many other sectors. The government needs to explore alternative ways of energy generation rather than the heavy reliance on imported oil. Originality/value Pakistan has been experiencing rising oil prices and housing prices with the rapid urbanisation and rural–urban migration. The contribution to the literature is that neither attempt (as to the best of the authors’ knowledge) has been made to check the impact of rising oil prices on housing sector development in Pakistan.
{"title":"The impact of oil price on housing prices: an empirical analysis of Pakistan","authors":"Rafiq Ahmed, H. Visas, Jabbar Ul-Haq","doi":"10.1108/ijhma-07-2022-0103","DOIUrl":"https://doi.org/10.1108/ijhma-07-2022-0103","url":null,"abstract":"\u0000Purpose\u0000This study aims to explore the impact of oil prices on housing prices using Pakistani annual data from 1973 to 2021.\u0000\u0000\u0000Design/methodology/approach\u0000The Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests were used for unit-root testing, whereas the johansen-juselius test was used for cointegration. For the short-run, the error correction model is used and the robustness of the model is checked using the dynamic ordinary least squares (DOLS) and fully modified OLS (FMOLS). The cumulative sum (CUSUM) and CUSUM of Squares tests were used to check the stability of the model, while parameter instability was confirmed by the Chow breakpoint test. Finally, the impulse response function was used for causality.\u0000\u0000\u0000Findings\u0000According to the findings, rising oil prices, among other things, have an impact on housing prices. Inflation is the single most important factor affecting not only the housing sector but also the entire economy. Lending and exchange rates have a significant impact on housing prices as well. The FMOLS and DOLS results suggest that the OLS results are robust. According to the variance decomposition model, housing prices and oil prices are bidirectionally related. The Government of Pakistan must develop a housing policy on a regular basis to develop the country’s urban housing supply and demand.\u0000\u0000\u0000Practical implications\u0000It is suggested that in Pakistan, the rising oil prices is a problem for the housing prices as well as many other sectors. The government needs to explore alternative ways of energy generation rather than the heavy reliance on imported oil.\u0000\u0000\u0000Originality/value\u0000Pakistan has been experiencing rising oil prices and housing prices with the rapid urbanisation and rural–urban migration. The contribution to the literature is that neither attempt (as to the best of the authors’ knowledge) has been made to check the impact of rising oil prices on housing sector development in Pakistan.\u0000","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45129460","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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