Pub Date : 2023-06-08DOI: 10.1108/ijhma-02-2023-0024
Vinayaka Gude
Purpose This research developed a model to understand and predict housing market dynamics and evaluate the significance of house permits data in the model’s forecasting capability. Design/methodology/approach The research uses a multilevel algorithm consisting of a machine-learning regression model to predict the independent variables and another regressor to predict the dependent variable using the forecasted independent variables. Findings The research establishes a statistically significant relationship between housing permits and house prices. The novel approach discussed in this paper has significantly higher prediction capabilities than a traditional regression model in forecasting monthly average prices (R-squared value: 0.5993), house price index prices (R-squared value: 0.99) and house sales prices (R-squared value: 0.7839). Research limitations/implications The impact of supply, demand and socioeconomic factors will differ in various regions. The forecasting capability and significance of the independent variables can vary, but the methodology can still be applicable when provided with the considered variables in the model. Practical implications The resulting model is helpful in the decision-making process for investments, house purchases and construction as the housing demand increases across various cities. The methodology can benefit multiple players, including the government, real estate investors, homebuyers and construction companies. Originality/value Existing algorithms and models do not consider the number of new house constructions, monthly sales and inventory in the real estate market, especially in the United States. This research aims to address these shortcomings using current socioeconomic indicators, permits, monthly real estate data and population information to predict house prices and inventory.
{"title":"A multi-level modeling approach for predicting real-estate dynamics","authors":"Vinayaka Gude","doi":"10.1108/ijhma-02-2023-0024","DOIUrl":"https://doi.org/10.1108/ijhma-02-2023-0024","url":null,"abstract":"\u0000Purpose\u0000This research developed a model to understand and predict housing market dynamics and evaluate the significance of house permits data in the model’s forecasting capability.\u0000\u0000\u0000Design/methodology/approach\u0000The research uses a multilevel algorithm consisting of a machine-learning regression model to predict the independent variables and another regressor to predict the dependent variable using the forecasted independent variables.\u0000\u0000\u0000Findings\u0000The research establishes a statistically significant relationship between housing permits and house prices. The novel approach discussed in this paper has significantly higher prediction capabilities than a traditional regression model in forecasting monthly average prices (R-squared value: 0.5993), house price index prices (R-squared value: 0.99) and house sales prices (R-squared value: 0.7839).\u0000\u0000\u0000Research limitations/implications\u0000The impact of supply, demand and socioeconomic factors will differ in various regions. The forecasting capability and significance of the independent variables can vary, but the methodology can still be applicable when provided with the considered variables in the model.\u0000\u0000\u0000Practical implications\u0000The resulting model is helpful in the decision-making process for investments, house purchases and construction as the housing demand increases across various cities. The methodology can benefit multiple players, including the government, real estate investors, homebuyers and construction companies.\u0000\u0000\u0000Originality/value\u0000Existing algorithms and models do not consider the number of new house constructions, monthly sales and inventory in the real estate market, especially in the United States. This research aims to address these shortcomings using current socioeconomic indicators, permits, monthly real estate data and population information to predict house prices and inventory.\u0000","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47998284","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 : 2023-06-07DOI: 10.1108/ijhma-03-2023-0036
Wenjing Li, Zhi Liu
Purpose In 2016, the Chinese central government decentralized the responsibilities of housing market regulation to the municipal level. This paper aims to assess whether the decentralized market regulation is effective. Design/methodology/approach This study first investigates the fundamental drivers of urban housing prices in China. Taking into consideration the factors driving housing prices, the authors further investigate the effectiveness of decentralized housing market regulation by a pre- and post-policy comparison test using a panel data set of 35 major cities for the years from 2014 to 2019. Findings The results reveal heterogenous policy effects on housing price growth among cities with a one-year lag in effectiveness. With the decentralized housing market regulation, cities with fast price growth are incentivized to implement tightening measures, while cities with relatively low housing prices and slow price growth are more likely to do nothing or deregulate the markets. The findings indicate that the shift from a centralized housing market regulation to a decentralized one is more appropriate and effective for the individual cities. Originality/value Few policy evaluation studies have been done to examine the effects of decentralized housing market regulation on the performance of urban housing markets in China. The authors devise a methodology to conduct a policy evaluation that is important to inform public policy and decisions. This study helps enhance the understanding of the fundamental factors in China’s urban housing markets and the effectiveness of municipal government interventions.
{"title":"Diversified urban housing markets and decentralized market regulation in China","authors":"Wenjing Li, Zhi Liu","doi":"10.1108/ijhma-03-2023-0036","DOIUrl":"https://doi.org/10.1108/ijhma-03-2023-0036","url":null,"abstract":"\u0000Purpose\u0000In 2016, the Chinese central government decentralized the responsibilities of housing market regulation to the municipal level. This paper aims to assess whether the decentralized market regulation is effective.\u0000\u0000\u0000Design/methodology/approach\u0000This study first investigates the fundamental drivers of urban housing prices in China. Taking into consideration the factors driving housing prices, the authors further investigate the effectiveness of decentralized housing market regulation by a pre- and post-policy comparison test using a panel data set of 35 major cities for the years from 2014 to 2019.\u0000\u0000\u0000Findings\u0000The results reveal heterogenous policy effects on housing price growth among cities with a one-year lag in effectiveness. With the decentralized housing market regulation, cities with fast price growth are incentivized to implement tightening measures, while cities with relatively low housing prices and slow price growth are more likely to do nothing or deregulate the markets. The findings indicate that the shift from a centralized housing market regulation to a decentralized one is more appropriate and effective for the individual cities.\u0000\u0000\u0000Originality/value\u0000Few policy evaluation studies have been done to examine the effects of decentralized housing market regulation on the performance of urban housing markets in China. The authors devise a methodology to conduct a policy evaluation that is important to inform public policy and decisions. This study helps enhance the understanding of the fundamental factors in China’s urban housing markets and the effectiveness of municipal government interventions.\u0000","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45766116","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 : 2023-06-06DOI: 10.1108/ijhma-01-2023-0015
Xingrui Zhang, Eunhwa Yang
Purpose Housing market research involves observing the relationships between housing value and its indicators. However, recent literature indicates that the disruption of the COVID-19 pandemic could have an impact on the forecasting properties of some of the housing indicators. This paper aims to observe the relationships between the home value index and three potential indicators to verify their forecasting properties pre- and post-COVID-19 and provide general recommendations for time series research post-pandemic. Design/methodology/approach This study features three vector autoregression (VAR) models constructed using the home value index of the USA, together with three indicators that are of interest according to recent literature: the national unemployment rate, private residential construction spending (PRCS) and the housing consumer price index (HCPI). Findings Unemployment, one of the prevalent indicators for housing values, was compromised as a result of the COVID-19 pandemic, and a new indicator for housing value in the USA, PRCS, whose relationship with housing value is robust even during the COVID-19 pandemic and HCPI is a more significant indicator for housing value than the prevalently cited All-Item consumer price index (CPI). Originality/value The study adds residential construction spending into the pool of housing indicators, proves that the finding of region-specific study indicating the unbounding of housing prices from unemployment is applicable to the aggregate housing market in the USA, and improves upon such widely accepted belief that overall inflation is a key indicator for housing prices and proves that the CPI for housing is a vastly more significant indicator.
{"title":"Have housing value indicators changed during COVID? Housing value prediction based on unemployment, construction spending, and housing consumer price index","authors":"Xingrui Zhang, Eunhwa Yang","doi":"10.1108/ijhma-01-2023-0015","DOIUrl":"https://doi.org/10.1108/ijhma-01-2023-0015","url":null,"abstract":"\u0000Purpose\u0000Housing market research involves observing the relationships between housing value and its indicators. However, recent literature indicates that the disruption of the COVID-19 pandemic could have an impact on the forecasting properties of some of the housing indicators. This paper aims to observe the relationships between the home value index and three potential indicators to verify their forecasting properties pre- and post-COVID-19 and provide general recommendations for time series research post-pandemic.\u0000\u0000\u0000Design/methodology/approach\u0000This study features three vector autoregression (VAR) models constructed using the home value index of the USA, together with three indicators that are of interest according to recent literature: the national unemployment rate, private residential construction spending (PRCS) and the housing consumer price index (HCPI).\u0000\u0000\u0000Findings\u0000Unemployment, one of the prevalent indicators for housing values, was compromised as a result of the COVID-19 pandemic, and a new indicator for housing value in the USA, PRCS, whose relationship with housing value is robust even during the COVID-19 pandemic and HCPI is a more significant indicator for housing value than the prevalently cited All-Item consumer price index (CPI).\u0000\u0000\u0000Originality/value\u0000The study adds residential construction spending into the pool of housing indicators, proves that the finding of region-specific study indicating the unbounding of housing prices from unemployment is applicable to the aggregate housing market in the USA, and improves upon such widely accepted belief that overall inflation is a key indicator for housing prices and proves that the CPI for housing is a vastly more significant indicator.\u0000","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44292652","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 : 2023-05-31DOI: 10.1108/ijhma-01-2023-0010
J. D. Oladeji, Benita Zulch (Kotze), J. Yacim
Purpose The challenge of accessibility to adequate housing in several countries by a large percentage of citizens has given rise to different housing programs designed to facilitate access to affordable housing. In South Africa, the National Housing Finance Corporation (NHFC) was created to provides housing loans to low- and middle-income earners. Thus, the purpose of this study was to evaluate the implication of the macroeconomic risk elements on the performance of the NHFC incremental housing finance. Design/methodology/approach This study used a mixed-method approach to examine the time-series data of the NHFC over 17 years (2003–2020), relative to selected macroeconomic indicators. Additionally, this study analysed primary data from a 2022 survey of NHFC Executives. Findings This study found that incremental housing finance addresses a housing affordability gap, caters to disadvantaged groups, adapts to changing macroeconomic conditions and can mitigate default risk. It also finds that the performance of the NHFC’s incremental housing finance is premised on the behaviour of the macroeconomic elements that drive its strategy in South Africa. Originality/value Unlike previous works on housing finance, this case study of the NHFC considers the implication of macroeconomic trends when disbursing incremental housing finance to low- and middle-level income earners as a risk mitigation measure for the South African market. Its mixed method use of quantitative and qualitative data also allows a robust insight into trends that drive investment in incremental housing finance in South Africa.
{"title":"Implications of macroeconomic risks on NHFC'S incremental housing finance in South Africa","authors":"J. D. Oladeji, Benita Zulch (Kotze), J. Yacim","doi":"10.1108/ijhma-01-2023-0010","DOIUrl":"https://doi.org/10.1108/ijhma-01-2023-0010","url":null,"abstract":"\u0000Purpose\u0000The challenge of accessibility to adequate housing in several countries by a large percentage of citizens has given rise to different housing programs designed to facilitate access to affordable housing. In South Africa, the National Housing Finance Corporation (NHFC) was created to provides housing loans to low- and middle-income earners. Thus, the purpose of this study was to evaluate the implication of the macroeconomic risk elements on the performance of the NHFC incremental housing finance.\u0000\u0000\u0000Design/methodology/approach\u0000This study used a mixed-method approach to examine the time-series data of the NHFC over 17 years (2003–2020), relative to selected macroeconomic indicators. Additionally, this study analysed primary data from a 2022 survey of NHFC Executives.\u0000\u0000\u0000Findings\u0000This study found that incremental housing finance addresses a housing affordability gap, caters to disadvantaged groups, adapts to changing macroeconomic conditions and can mitigate default risk. It also finds that the performance of the NHFC’s incremental housing finance is premised on the behaviour of the macroeconomic elements that drive its strategy in South Africa.\u0000\u0000\u0000Originality/value\u0000Unlike previous works on housing finance, this case study of the NHFC considers the implication of macroeconomic trends when disbursing incremental housing finance to low- and middle-level income earners as a risk mitigation measure for the South African market. Its mixed method use of quantitative and qualitative data also allows a robust insight into trends that drive investment in incremental housing finance in South Africa.\u0000","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46299382","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 : 2023-05-25DOI: 10.1108/ijhma-03-2023-0041
Alesia Gerassimenko, Laurens Defau, Lieven De Moor
Purpose The current literature on energy certificates shows that Energy Performance Certificate labels have an important effect on real estate prices. However, interestingly, the limited studies that address the rental market find significantly lower price premiums than the sales market. The purpose of this paper is to add to this literature, by doing a comparative analysis of price premiums in the sales and rental market in Flanders (Belgium). Design/methodology/approach This study uses a hedonic regression model to analyze 177,670 real estate listings between 2016 and 2021. The data is provided by Immoweb – the largest online real estate platform in Belgium. The data set was divided in sold and rented properties: the authors evaluated 126,217 sales listings and 51,453 rent listings. Findings The results confirm that energy efficient properties generate a price premium, but that this premium is significantly larger in the sales market than in the rental market. In addition, the findings indicate that both investors and landlords could benefit strongly from renovating dwellings – especially when renovating from an F label to an A label. Originality/value Previous research focuses strongly on the sales market, although in many countries the rental market is similar in size and responsible from much energy consumption. Interestingly, the few studies that are addressing the rental market, find singificantly smaller price premiums than in the sales market. The findings add to this literature tradition and offer a comparative analysis of price premiums in the sales and rental market in Flanders. This allows us to not only show the similarities between both markets but also highlight the differences – creating valuable insights for academia, governments and real estate professionals.
{"title":"The impact of energy certificates on sales and rental prices: a comparative analysis","authors":"Alesia Gerassimenko, Laurens Defau, Lieven De Moor","doi":"10.1108/ijhma-03-2023-0041","DOIUrl":"https://doi.org/10.1108/ijhma-03-2023-0041","url":null,"abstract":"\u0000Purpose\u0000The current literature on energy certificates shows that Energy Performance Certificate labels have an important effect on real estate prices. However, interestingly, the limited studies that address the rental market find significantly lower price premiums than the sales market. The purpose of this paper is to add to this literature, by doing a comparative analysis of price premiums in the sales and rental market in Flanders (Belgium).\u0000\u0000\u0000Design/methodology/approach\u0000This study uses a hedonic regression model to analyze 177,670 real estate listings between 2016 and 2021. The data is provided by Immoweb – the largest online real estate platform in Belgium. The data set was divided in sold and rented properties: the authors evaluated 126,217 sales listings and 51,453 rent listings.\u0000\u0000\u0000Findings\u0000The results confirm that energy efficient properties generate a price premium, but that this premium is significantly larger in the sales market than in the rental market. In addition, the findings indicate that both investors and landlords could benefit strongly from renovating dwellings – especially when renovating from an F label to an A label.\u0000\u0000\u0000Originality/value\u0000Previous research focuses strongly on the sales market, although in many countries the rental market is similar in size and responsible from much energy consumption. Interestingly, the few studies that are addressing the rental market, find singificantly smaller price premiums than in the sales market. The findings add to this literature tradition and offer a comparative analysis of price premiums in the sales and rental market in Flanders. This allows us to not only show the similarities between both markets but also highlight the differences – creating valuable insights for academia, governments and real estate professionals.\u0000","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48695012","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 : 2023-05-17DOI: 10.1108/ijhma-03-2023-0035
A. Rashad, Mahmoud Farghally
Purpose The monetary policy is an important driver of the real estate sector’s performance. The recent wave of monetary tightening in 2022 in response to the cost-of-living crisis has been associated with the decline in housing prices across the globe. There are two main channels through which the US monetary policy may affect the real estate market in the dollar-pegged countries: the cost of serving mortgages (financing cost) and the exchange rate channel (for example, the appreciation of the US dollar and consequently the local currency). The exchange rate channel, which involves the appreciation of the US dollar and the subsequent effect on the local currency, is particularly significant in the case of Dubai, given how international the housing market in Dubai and might be viewed as a tradable good. Using recent data, the purpose of this study to evaluate the spillover impact of the US monetary policy on the housing market performance in the dollar-pegged countries using Dubai as a case study. Design/methodology/approach For this purpose, this study collected unique longitudinal data on the volume of the monthly transactions of residential properties and performs a panel-data analysis using within-variation models. The changes in the interest rate policy in the USA are determined by the domestic inflation in the USA, thereby, representing an exogenous shock in the UAE. Findings The results are robust to different specifications and suggest that a strong negative correlation between the interest rate in the USA and the housing sector demand in Dubai. Fiscal policy measures can be taken to mitigate tighter financial conditions in case of policy misalignment. Originality/value Few studies have looked at the spillover impact of the global monetary conditions on the real estate market in the GCC region. This study fills this gap by exploring the impact of the US financial conditions on Dubai’s real estate, using panel data analysis.
{"title":"The US monetary conditions and Dubai’s real estate market: twist or tango?","authors":"A. Rashad, Mahmoud Farghally","doi":"10.1108/ijhma-03-2023-0035","DOIUrl":"https://doi.org/10.1108/ijhma-03-2023-0035","url":null,"abstract":"\u0000Purpose\u0000The monetary policy is an important driver of the real estate sector’s performance. The recent wave of monetary tightening in 2022 in response to the cost-of-living crisis has been associated with the decline in housing prices across the globe. There are two main channels through which the US monetary policy may affect the real estate market in the dollar-pegged countries: the cost of serving mortgages (financing cost) and the exchange rate channel (for example, the appreciation of the US dollar and consequently the local currency). The exchange rate channel, which involves the appreciation of the US dollar and the subsequent effect on the local currency, is particularly significant in the case of Dubai, given how international the housing market in Dubai and might be viewed as a tradable good. Using recent data, the purpose of this study to evaluate the spillover impact of the US monetary policy on the housing market performance in the dollar-pegged countries using Dubai as a case study.\u0000\u0000\u0000Design/methodology/approach\u0000For this purpose, this study collected unique longitudinal data on the volume of the monthly transactions of residential properties and performs a panel-data analysis using within-variation models. The changes in the interest rate policy in the USA are determined by the domestic inflation in the USA, thereby, representing an exogenous shock in the UAE.\u0000\u0000\u0000Findings\u0000The results are robust to different specifications and suggest that a strong negative correlation between the interest rate in the USA and the housing sector demand in Dubai. Fiscal policy measures can be taken to mitigate tighter financial conditions in case of policy misalignment.\u0000\u0000\u0000Originality/value\u0000Few studies have looked at the spillover impact of the global monetary conditions on the real estate market in the GCC region. This study fills this gap by exploring the impact of the US financial conditions on Dubai’s real estate, using panel data analysis.\u0000","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43755857","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 : 2023-05-08DOI: 10.1108/ijhma-01-2023-0016
N. Gopy-Ramdhany, B. Seetanah
Purpose Mauritius’s residential real estate sector has undergone an increase in foreign investment over the past decades. This study aims to establish if the increasing level of foreign real estate investments (FREI) has increased land demand and land prices. The study also aims to depict whether the relation between FREI and land prices prevails at an aggregate and/ or a regional level. Design/methodology/approach Data from 26 regions, classified as urban, rural and coastal is collected on an annual basis over the period 2000 to 2019, and a dynamic panel regression framework, namely, an autoregressive distributed lag model, is used to take into account the dynamic nature of land price modeling. Findings The findings show that, at the aggregate level, in the long-term, FREI does not have a significant influence on land prices, while in the short term, a positive significant relationship is noted between the two variables. A regional breakdown of the data into urban, rural and coastal was done. In the long term, only in coastal regions, a positive significant link was observed, whereas in urban and rural regions FREI did not influence land prices. In the short term, the positive link subsists in the coastal regions, and in rural regions also land prices are positively affected by FREI. Originality/value Unlike other studies which have used quite general measures of FREI, the present research has focused on FREI mainly undertaken in the residential real estate market and how these have affected residential land prices. This study also contributes to research on the determinants of land prices which is relatively scarce compared to research on housing prices.
{"title":"A dynamic analysis of the influence of foreign real estate investments on residential land prices in Mauritius","authors":"N. Gopy-Ramdhany, B. Seetanah","doi":"10.1108/ijhma-01-2023-0016","DOIUrl":"https://doi.org/10.1108/ijhma-01-2023-0016","url":null,"abstract":"\u0000Purpose\u0000Mauritius’s residential real estate sector has undergone an increase in foreign investment over the past decades. This study aims to establish if the increasing level of foreign real estate investments (FREI) has increased land demand and land prices. The study also aims to depict whether the relation between FREI and land prices prevails at an aggregate and/ or a regional level.\u0000\u0000\u0000Design/methodology/approach\u0000Data from 26 regions, classified as urban, rural and coastal is collected on an annual basis over the period 2000 to 2019, and a dynamic panel regression framework, namely, an autoregressive distributed lag model, is used to take into account the dynamic nature of land price modeling.\u0000\u0000\u0000Findings\u0000The findings show that, at the aggregate level, in the long-term, FREI does not have a significant influence on land prices, while in the short term, a positive significant relationship is noted between the two variables. A regional breakdown of the data into urban, rural and coastal was done. In the long term, only in coastal regions, a positive significant link was observed, whereas in urban and rural regions FREI did not influence land prices. In the short term, the positive link subsists in the coastal regions, and in rural regions also land prices are positively affected by FREI.\u0000\u0000\u0000Originality/value\u0000Unlike other studies which have used quite general measures of FREI, the present research has focused on FREI mainly undertaken in the residential real estate market and how these have affected residential land prices. This study also contributes to research on the determinants of land prices which is relatively scarce compared to research on housing prices.\u0000","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41400250","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 : 2023-05-05DOI: 10.1108/ijhma-02-2023-0020
Laura H. Atuesta, Monserrat Carrasco
Purpose Between 2006 and 2012, Mexico implemented a “frontal war against organized crime”. This strategy increased criminal violence and triggered negative consequences across the country’s economic, political and social spheres. This study aims to analyse how the magnitude and visibility of criminal violence impact the housing market of Mexico City. Design/methodology/approach The authors used different violent proxies to measure the effect of the magnitude and visibility of violence in housing prices. The structure of the data set is an unbalanced panel with no conditions of strict exogeneity. To address endogeneity, the authors calculate the first differences to estimate an Arellano–Bond estimator and use the lags of the dependent variable to instrumentalise the endogenous variable. Findings Results suggest that the magnitude of violence negatively impacts housing prices. Similarly, housing prices are negatively affected the closer the property is to visible violence, measured through narcomessages placed next to the bodies of executed victims. Lastly, housing prices are not always affected when a violent event occurs nearby, specifically, when neighbours or potential buyers consider this event as sporadic violence. Originality/value There are only a few studies of violence in housing prices using data from developing countries, and most of these studies are conducted with aggregated data at the municipality or state level. The authors are using geocoded information, both violence events and housing prices, to estimate more disaggregated effects. Moreover, the authors used different proxies to measure different characteristics of violence (magnitude and visibility) to estimate the heterogeneous effects of violence on housing prices.
{"title":"How frequent and visible criminal violence affects housing prices: evidence from Mexico City (2007–2011)","authors":"Laura H. Atuesta, Monserrat Carrasco","doi":"10.1108/ijhma-02-2023-0020","DOIUrl":"https://doi.org/10.1108/ijhma-02-2023-0020","url":null,"abstract":"\u0000Purpose\u0000Between 2006 and 2012, Mexico implemented a “frontal war against organized crime”. This strategy increased criminal violence and triggered negative consequences across the country’s economic, political and social spheres. This study aims to analyse how the magnitude and visibility of criminal violence impact the housing market of Mexico City.\u0000\u0000\u0000Design/methodology/approach\u0000The authors used different violent proxies to measure the effect of the magnitude and visibility of violence in housing prices. The structure of the data set is an unbalanced panel with no conditions of strict exogeneity. To address endogeneity, the authors calculate the first differences to estimate an Arellano–Bond estimator and use the lags of the dependent variable to instrumentalise the endogenous variable.\u0000\u0000\u0000Findings\u0000Results suggest that the magnitude of violence negatively impacts housing prices. Similarly, housing prices are negatively affected the closer the property is to visible violence, measured through narcomessages placed next to the bodies of executed victims. Lastly, housing prices are not always affected when a violent event occurs nearby, specifically, when neighbours or potential buyers consider this event as sporadic violence.\u0000\u0000\u0000Originality/value\u0000There are only a few studies of violence in housing prices using data from developing countries, and most of these studies are conducted with aggregated data at the municipality or state level. The authors are using geocoded information, both violence events and housing prices, to estimate more disaggregated effects. Moreover, the authors used different proxies to measure different characteristics of violence (magnitude and visibility) to estimate the heterogeneous effects of violence on housing prices.\u0000","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43466124","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 : 2023-04-19DOI: 10.1108/ijhma-02-2023-0018
S. Herath, V. Mangioni, S. Shi, X. Ge
Purpose House price fluctuations send vital signals to many parts of the economy, and long-term predictions of house prices are of great interest to governments and property developers. Although predictive models based on economic fundamentals are widely used, the common requirement for such studies is that underlying data are stationary. This paper aims to demonstrate the usefulness of alternative filtering methods for forecasting house prices. Design/methodology/approach We specifically focus on exponential smoothing with trend adjustment and multiplicative decomposition using median house prices for Sydney from Q3 1994 to Q1 2017. The model performance is evaluated using out-of-sample forecasting techniques and a robustness check against secondary data sources. Findings Multiplicative decomposition outperforms exponential smoothing at forecasting accuracy. The superior decomposition model suggests that seasonal and cyclical components provide important additional information for predicting house prices. The forecasts for 2017–2028 suggest that prices will slowly increase, going past 2016 levels by 2020 in the apartment market and by 2022/2023 in the detached housing market. Research limitations/implications We demonstrate that filtering models are simple (univariate models that only require historical house prices), easy to implement (with no condition of stationarity) and widely used in financial trading, sports betting and other fields where producing accurate forecasts is more important than explaining the drivers of change. The paper puts forward a case for the inclusion of filtering models within the forecasting toolkit as a useful reference point for comparing forecasts from alternative models. Originality/value To the best of the authors’ knowledge, this paper undertakes the first systematic comparison of two filtering models for the Sydney housing market.
{"title":"Extrapolative time-series modelling of house prices: a case study from Sydney, Australia","authors":"S. Herath, V. Mangioni, S. Shi, X. Ge","doi":"10.1108/ijhma-02-2023-0018","DOIUrl":"https://doi.org/10.1108/ijhma-02-2023-0018","url":null,"abstract":"\u0000Purpose\u0000House price fluctuations send vital signals to many parts of the economy, and long-term predictions of house prices are of great interest to governments and property developers. Although predictive models based on economic fundamentals are widely used, the common requirement for such studies is that underlying data are stationary. This paper aims to demonstrate the usefulness of alternative filtering methods for forecasting house prices.\u0000\u0000\u0000Design/methodology/approach\u0000We specifically focus on exponential smoothing with trend adjustment and multiplicative decomposition using median house prices for Sydney from Q3 1994 to Q1 2017. The model performance is evaluated using out-of-sample forecasting techniques and a robustness check against secondary data sources.\u0000\u0000\u0000Findings\u0000Multiplicative decomposition outperforms exponential smoothing at forecasting accuracy. The superior decomposition model suggests that seasonal and cyclical components provide important additional information for predicting house prices. The forecasts for 2017–2028 suggest that prices will slowly increase, going past 2016 levels by 2020 in the apartment market and by 2022/2023 in the detached housing market.\u0000\u0000\u0000Research limitations/implications\u0000We demonstrate that filtering models are simple (univariate models that only require historical house prices), easy to implement (with no condition of stationarity) and widely used in financial trading, sports betting and other fields where producing accurate forecasts is more important than explaining the drivers of change. The paper puts forward a case for the inclusion of filtering models within the forecasting toolkit as a useful reference point for comparing forecasts from alternative models.\u0000\u0000\u0000Originality/value\u0000To the best of the authors’ knowledge, this paper undertakes the first systematic comparison of two filtering models for the Sydney housing market.\u0000","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42788938","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 : 2023-04-17DOI: 10.1108/ijhma-02-2023-0017
G. J. Rangel, J. Ng, T. T. Murugasu, W. Poon
Purpose The purpose of this study is to use a lifetime income measure to evaluate the long-run housing affordability for an understudied cohort of households in the literature – the millennials. The authors do this in the context of Malaysia, measuring long-run affordability for four housing types across geographic locations and income distributions. Design/methodology/approach This study calculates a long-run housing affordability index (HAI) using data on house prices and household incomes. Essentially a ratio of predicted lifetime incomes to house prices, the HAI is computed for four common housing types in Malaysia from 2005 to 2016 and for six states in the country. The HAI is also compared across four income percentiles. Findings The analysis reveals varying patterns of housing affordability among different states in Malaysia. Housing affordability has declined since 2010, with most housing types being unaffordable for millennial-led households with the lowest income. Housing is most affordable for those in the highest income bracket, although even here, there are pockets of unaffordable housing as well. Practical implications Based on the findings, this study proposes three targeted interventions to improve housing affordability for Malaysian millennials. Originality/value This study fills a gap in the literature by examining the long-run housing affordability of Malaysian millennial-led households based on both geographic location and income distribution. The millennial population is understudied in the housing affordability literature, making this study a valuable contribution to the field.
{"title":"Measuring long-run housing affordability for Malaysian millennial households: a geospatial and income distribution analysis","authors":"G. J. Rangel, J. Ng, T. T. Murugasu, W. Poon","doi":"10.1108/ijhma-02-2023-0017","DOIUrl":"https://doi.org/10.1108/ijhma-02-2023-0017","url":null,"abstract":"\u0000Purpose\u0000The purpose of this study is to use a lifetime income measure to evaluate the long-run housing affordability for an understudied cohort of households in the literature – the millennials. The authors do this in the context of Malaysia, measuring long-run affordability for four housing types across geographic locations and income distributions.\u0000\u0000\u0000Design/methodology/approach\u0000This study calculates a long-run housing affordability index (HAI) using data on house prices and household incomes. Essentially a ratio of predicted lifetime incomes to house prices, the HAI is computed for four common housing types in Malaysia from 2005 to 2016 and for six states in the country. The HAI is also compared across four income percentiles.\u0000\u0000\u0000Findings\u0000The analysis reveals varying patterns of housing affordability among different states in Malaysia. Housing affordability has declined since 2010, with most housing types being unaffordable for millennial-led households with the lowest income. Housing is most affordable for those in the highest income bracket, although even here, there are pockets of unaffordable housing as well.\u0000\u0000\u0000Practical implications\u0000Based on the findings, this study proposes three targeted interventions to improve housing affordability for Malaysian millennials.\u0000\u0000\u0000Originality/value\u0000This study fills a gap in the literature by examining the long-run housing affordability of Malaysian millennial-led households based on both geographic location and income distribution. The millennial population is understudied in the housing affordability literature, making this study a valuable contribution to the field.\u0000","PeriodicalId":14136,"journal":{"name":"International Journal of Housing Markets and Analysis","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45983761","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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