Pub Date : 2024-07-01DOI: 10.3390/realestate1020008
Zahra Ahmadi, Bjorn Berggren, Mohammad Ismail, Lars Silver
Public Housing Companies (PHCs) play an important role in the Swedish housing market, with approximately 300 companies managing circa 802,000 dwellings. The public housing sector thereby represents almost 20 percent of the total housing stock in Sweden and half of the apartments that are available for rental. The purpose of this paper is to analyze the most important factors behind the profitability in Swedish PHCs between 2010 and 2019. The effects of internal growth, age, and capital structure in the PHCs are analyzed together with the effect of the growth of the local market, as well as local rent levels. Financial information for circa 300 PHCs in Sweden was gathered from annual reports published between 2010 to 2019. The financial information was analyzed using panel data analysis methods with several explanatory variables to explain the financial performance of the PHCs. The results from the analysis indicate a highly significant and positive relationship between the annual change in population, age, and profitability in the PHC. A highly significant and negative relationship was found between the PHC internal growth, capital structure, and profitability. The results showed no significant relationship between changes in income, rent levels, and profitability in Swedish PHC.
{"title":"Profitability in Public Housing Companies: A Longitudinal and Regional Analysis Using Swedish Panel Data","authors":"Zahra Ahmadi, Bjorn Berggren, Mohammad Ismail, Lars Silver","doi":"10.3390/realestate1020008","DOIUrl":"https://doi.org/10.3390/realestate1020008","url":null,"abstract":"Public Housing Companies (PHCs) play an important role in the Swedish housing market, with approximately 300 companies managing circa 802,000 dwellings. The public housing sector thereby represents almost 20 percent of the total housing stock in Sweden and half of the apartments that are available for rental. The purpose of this paper is to analyze the most important factors behind the profitability in Swedish PHCs between 2010 and 2019. The effects of internal growth, age, and capital structure in the PHCs are analyzed together with the effect of the growth of the local market, as well as local rent levels. Financial information for circa 300 PHCs in Sweden was gathered from annual reports published between 2010 to 2019. The financial information was analyzed using panel data analysis methods with several explanatory variables to explain the financial performance of the PHCs. The results from the analysis indicate a highly significant and positive relationship between the annual change in population, age, and profitability in the PHC. A highly significant and negative relationship was found between the PHC internal growth, capital structure, and profitability. The results showed no significant relationship between changes in income, rent levels, and profitability in Swedish PHC.","PeriodicalId":506214,"journal":{"name":"Real Estate","volume":"28 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141710948","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 : 2024-03-20DOI: 10.3390/realestate1010005
T. B. Odubiyi, R. Abidoye, C. Aigbavboa, W. Thwala, Adeyemi Samuel Ademiloye, O. Oshodi
In recent years, scholars have called for an increase in the usage of green features in the built environment to address climate change issues. Governments across the developed world are implementing legislation to support this increased uptake. However, little is known about how the inclusion of green features influences the rental value of residential properties located in developing countries. Data on 389 residential properties were extracted and collected from a webpage. Text mining and machine learning models were used to evaluate the impact of green features on the rental value of residential properties. The results indicated that floor area, number of bathrooms, and availability of furniture are the top three attributes affecting the rental value of residential properties. The random forest model generated better predictions when compared with other modelling techniques. It was also observed that green features are not the most common words mentioned in rental adverts for residential properties. The results suggest that green features add limited value to residential properties in South Africa. This finding suggests that there is a need for stakeholders to create and implement policies targeted at incentivising the inclusion of green features in existing and new residential properties in South Africa.
{"title":"Impact of Green Features on Rental Value of Residential Properties: Evidence from South Africa","authors":"T. B. Odubiyi, R. Abidoye, C. Aigbavboa, W. Thwala, Adeyemi Samuel Ademiloye, O. Oshodi","doi":"10.3390/realestate1010005","DOIUrl":"https://doi.org/10.3390/realestate1010005","url":null,"abstract":"In recent years, scholars have called for an increase in the usage of green features in the built environment to address climate change issues. Governments across the developed world are implementing legislation to support this increased uptake. However, little is known about how the inclusion of green features influences the rental value of residential properties located in developing countries. Data on 389 residential properties were extracted and collected from a webpage. Text mining and machine learning models were used to evaluate the impact of green features on the rental value of residential properties. The results indicated that floor area, number of bathrooms, and availability of furniture are the top three attributes affecting the rental value of residential properties. The random forest model generated better predictions when compared with other modelling techniques. It was also observed that green features are not the most common words mentioned in rental adverts for residential properties. The results suggest that green features add limited value to residential properties in South Africa. This finding suggests that there is a need for stakeholders to create and implement policies targeted at incentivising the inclusion of green features in existing and new residential properties in South Africa.","PeriodicalId":506214,"journal":{"name":"Real Estate","volume":"351 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140227968","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 : 2024-03-12DOI: 10.3390/realestate1010004
Steven B. Caudill, Neela D. Manage, Franklin G. Mixon
Hedonic house price studies typically incorporate information about location by including either a set of dummy variables to represent individual locations called “neighborhoods” or by using a set of distance (or travel time) variables to characterize locations in terms of proximity to amenities and dis-amenities. As an alternative to these, relatively recent research advocates a latitude–longitude co-ordinate system for incorporating distance information into hedonic house price regressions. This study shows that many of the claims made in this research, particularly those referencing the elimination or diminution of “biases of coefficients of non-distance variables”, are given the particulars of the Monte Carlo experiments, not possible to investigate. We further show, both analytically and with our simulations, that there is no omitted variable bias present in their simulations because their randomly generated non-distance variable is uncorrelated with any of the other variables used in their regression models.
{"title":"Using Co-Ordinate Systems in Hedonic Housing Regressions","authors":"Steven B. Caudill, Neela D. Manage, Franklin G. Mixon","doi":"10.3390/realestate1010004","DOIUrl":"https://doi.org/10.3390/realestate1010004","url":null,"abstract":"Hedonic house price studies typically incorporate information about location by including either a set of dummy variables to represent individual locations called “neighborhoods” or by using a set of distance (or travel time) variables to characterize locations in terms of proximity to amenities and dis-amenities. As an alternative to these, relatively recent research advocates a latitude–longitude co-ordinate system for incorporating distance information into hedonic house price regressions. This study shows that many of the claims made in this research, particularly those referencing the elimination or diminution of “biases of coefficients of non-distance variables”, are given the particulars of the Monte Carlo experiments, not possible to investigate. We further show, both analytically and with our simulations, that there is no omitted variable bias present in their simulations because their randomly generated non-distance variable is uncorrelated with any of the other variables used in their regression models.","PeriodicalId":506214,"journal":{"name":"Real Estate","volume":"181 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140249959","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 : 2024-01-31DOI: 10.3390/realestate1010003
Pierfrancesco De Paola
Accuracy in property valuations is a fundamental element in the real estate market for making informed decisions and developing effective investment strategies. The complex dynamics of real estate markets, coupled with the high differentiation of properties, scarcity, and opaqueness of real estate data, underscore the importance of adopting advanced approaches to obtain accurate valuations, especially with small property samples. The objective of this study is to explore the applicability of the Maximum Entropy Principle to real estate valuations with the support of Lagrange multipliers, emphasizing how this methodology can significantly enhance valuation precision, particularly with a small real estate sample. The excellent results obtained suggest that the Maximum Entropy Principle with Lagrange multipliers can be successfully employed for real estate valuations. In the case study, the average prediction error for sales prices ranged from 5.12% to 6.91%, indicating a very high potential for its application in real estate valuations. Compared to other established methodologies, the Maximum Entropy Principle with Lagrange multipliers aims to be a valid alternative with superior advantages.
{"title":"Real Estate Valuations with Small Dataset: A Novel Method Based on the Maximum Entropy Principle and Lagrange Multipliers","authors":"Pierfrancesco De Paola","doi":"10.3390/realestate1010003","DOIUrl":"https://doi.org/10.3390/realestate1010003","url":null,"abstract":"Accuracy in property valuations is a fundamental element in the real estate market for making informed decisions and developing effective investment strategies. The complex dynamics of real estate markets, coupled with the high differentiation of properties, scarcity, and opaqueness of real estate data, underscore the importance of adopting advanced approaches to obtain accurate valuations, especially with small property samples. The objective of this study is to explore the applicability of the Maximum Entropy Principle to real estate valuations with the support of Lagrange multipliers, emphasizing how this methodology can significantly enhance valuation precision, particularly with a small real estate sample. The excellent results obtained suggest that the Maximum Entropy Principle with Lagrange multipliers can be successfully employed for real estate valuations. In the case study, the average prediction error for sales prices ranged from 5.12% to 6.91%, indicating a very high potential for its application in real estate valuations. Compared to other established methodologies, the Maximum Entropy Principle with Lagrange multipliers aims to be a valid alternative with superior advantages.","PeriodicalId":506214,"journal":{"name":"Real Estate","volume":"109 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140470952","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 : 2023-12-12DOI: 10.3390/realestate1010002
Mats Wilhelmsson
This study investigates why young adults live with their parents in Sweden. As young adults’ living arrangements affect decisions about marriage, education, childbirth, and participation in the workforce, more knowledge for policymakers is crucial to implementing effective policies to support young adults and promote financial independence and well-being. Using a data set from 1998 to 2021 at the municipal level in Sweden, we used a spatial autoregressive panel data model to examine the proportion of young adults living at home and the regional disparities. The study uncovered intraregional variations that illustrate how different municipalities in Sweden exhibit different patterns of young adults living at home. Our findings reveal that economic factors such as unemployment significantly impact this pattern. Housing market dynamics, demographic factors, cultural differences, and location-specific characteristics also play an essential role in explaining this pattern. These findings suggest that the key drivers are the lack of rental housing, high unemployment rates, a high degree of urbanisation, interregional migration, and social capital (such as social cohesion and inclusion).
{"title":"Housing Choices of Young Adults in Sweden","authors":"Mats Wilhelmsson","doi":"10.3390/realestate1010002","DOIUrl":"https://doi.org/10.3390/realestate1010002","url":null,"abstract":"This study investigates why young adults live with their parents in Sweden. As young adults’ living arrangements affect decisions about marriage, education, childbirth, and participation in the workforce, more knowledge for policymakers is crucial to implementing effective policies to support young adults and promote financial independence and well-being. Using a data set from 1998 to 2021 at the municipal level in Sweden, we used a spatial autoregressive panel data model to examine the proportion of young adults living at home and the regional disparities. The study uncovered intraregional variations that illustrate how different municipalities in Sweden exhibit different patterns of young adults living at home. Our findings reveal that economic factors such as unemployment significantly impact this pattern. Housing market dynamics, demographic factors, cultural differences, and location-specific characteristics also play an essential role in explaining this pattern. These findings suggest that the key drivers are the lack of rental housing, high unemployment rates, a high degree of urbanisation, interregional migration, and social capital (such as social cohesion and inclusion).","PeriodicalId":506214,"journal":{"name":"Real Estate","volume":"118 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139183140","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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