Abstract The objective of the paper is to verify hypotheses regarding integration and cointegration (relation) of mean apartment prices on the primary and secondary market in Szczecin. Both transaction prices as well as offer prices of apartments were investigated. The analysis period encompasses the years of 2006 – 2022 (quarterly data). An ADF test was employed to examine the integration of time series, taking into consideration a deterministic component in the form of a quadratic function. Only the time series of mean offer prices and transaction prices on the primary market proved to be integrated in the first degree. The time series of mean offer prices and transaction prices on the secondary market were not integrated, they occurred to be trend stationary series. A two-step Engle-Granger test was employed to analyze the cointegration of time series, which confirmed the cointegration of mean offer prices and transaction prices on the primary market. The relations between individual price types were examined with the use of a procedure which entailed analyzing (with an ADF test) difference stationarity between prices. From the empirical studies it arises that, in Szczecin, transaction and offer prices on the primary market follow one another. On the secondary market, offer and transaction prices are trend stationary and they converge. On the other hand, prices on the primary market diverge from prices on the secondary market (the primary market diverges from the secondary market). This concerns both offer prices as well as transaction prices.
{"title":"Integration and Cointegration of Apartment Prices on the Primary and Secondary Market in Szczecin in the Years 2006-2022","authors":"M. Doszyń","doi":"10.2478/remav-2023-0028","DOIUrl":"https://doi.org/10.2478/remav-2023-0028","url":null,"abstract":"Abstract The objective of the paper is to verify hypotheses regarding integration and cointegration (relation) of mean apartment prices on the primary and secondary market in Szczecin. Both transaction prices as well as offer prices of apartments were investigated. The analysis period encompasses the years of 2006 – 2022 (quarterly data). An ADF test was employed to examine the integration of time series, taking into consideration a deterministic component in the form of a quadratic function. Only the time series of mean offer prices and transaction prices on the primary market proved to be integrated in the first degree. The time series of mean offer prices and transaction prices on the secondary market were not integrated, they occurred to be trend stationary series. A two-step Engle-Granger test was employed to analyze the cointegration of time series, which confirmed the cointegration of mean offer prices and transaction prices on the primary market. The relations between individual price types were examined with the use of a procedure which entailed analyzing (with an ADF test) difference stationarity between prices. From the empirical studies it arises that, in Szczecin, transaction and offer prices on the primary market follow one another. On the secondary market, offer and transaction prices are trend stationary and they converge. On the other hand, prices on the primary market diverge from prices on the secondary market (the primary market diverges from the secondary market). This concerns both offer prices as well as transaction prices.","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":"12 5","pages":"36 - 44"},"PeriodicalIF":0.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138624980","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}
Abstract The issue of similarity in the real estate market is a widely recognized aspect of analysis, yet it remains underexplored in scientific research. This study aims to address this gap by introducing the concept of a Property Cognitive Information System (PCIS), which offers an innovative approach to analyzing similarity in the real estate market. The PCIS introduces non-classical and alternative solutions, departing from the conventional data analysis practices commonly employed in the real estate market. Moreover, the study delves into the integration of artificial intelligence (AI) in the PCIS. The paper highlights the value added by the PCIS, specifically discussing the validity of using automatic ML-based solutions to objectify the results of synergistic data processing in the real estate market. Furthermore, the article establishes a set of essential assumptions and recommendations that contribute to a well-defined and interpretable notion of similarity in the context of human-machine analyses. By exploring the intricacies of similarity in the real estate market through the innovative PCIS and AI-based solutions, this research seeks to broaden the understanding and applicability of data analysis techniques in this domain.
{"title":"Human-Machine Synergy in Real Estate Similarity Concept","authors":"M. Renigier‐Biłozor, Artur Janowski","doi":"10.2478/remav-2024-0010","DOIUrl":"https://doi.org/10.2478/remav-2024-0010","url":null,"abstract":"Abstract The issue of similarity in the real estate market is a widely recognized aspect of analysis, yet it remains underexplored in scientific research. This study aims to address this gap by introducing the concept of a Property Cognitive Information System (PCIS), which offers an innovative approach to analyzing similarity in the real estate market. The PCIS introduces non-classical and alternative solutions, departing from the conventional data analysis practices commonly employed in the real estate market. Moreover, the study delves into the integration of artificial intelligence (AI) in the PCIS. The paper highlights the value added by the PCIS, specifically discussing the validity of using automatic ML-based solutions to objectify the results of synergistic data processing in the real estate market. Furthermore, the article establishes a set of essential assumptions and recommendations that contribute to a well-defined and interpretable notion of similarity in the context of human-machine analyses. By exploring the intricacies of similarity in the real estate market through the innovative PCIS and AI-based solutions, this research seeks to broaden the understanding and applicability of data analysis techniques in this domain.","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":"45 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139235079","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}
Sylwester J. Rzeszut, Michał J. Kowalski, Jan K. Kazak
Abstract The pandemic, followed by the Russian aggression against Ukraine, caused rapid changes in the economy. European countries experienced unprecedented price increases, which resulted in a significant increase in the cost of capital. This resulted primarily in limited access to capital and a significant reduction in investments in the real estate market. In addition, investors began to withdraw capital from investments in the real estate market to other assets, encouraged by their rising rates of return. The article presents how the indicated circumstances translated into the financial efficiency of companies from the Real Estate sector. Listed companies of the European Economic Area in the years 2019-2022 were analyzed. Changes in the main accounting measures and market measures for individual countries as well as the characteristics of real estate market participants were analyzed.
{"title":"Financial Efficiency and Investor Behavior on the European Real Estate Market in the Rising Inflation Environment","authors":"Sylwester J. Rzeszut, Michał J. Kowalski, Jan K. Kazak","doi":"10.2478/remav-2024-0007","DOIUrl":"https://doi.org/10.2478/remav-2024-0007","url":null,"abstract":"Abstract The pandemic, followed by the Russian aggression against Ukraine, caused rapid changes in the economy. European countries experienced unprecedented price increases, which resulted in a significant increase in the cost of capital. This resulted primarily in limited access to capital and a significant reduction in investments in the real estate market. In addition, investors began to withdraw capital from investments in the real estate market to other assets, encouraged by their rising rates of return. The article presents how the indicated circumstances translated into the financial efficiency of companies from the Real Estate sector. Listed companies of the European Economic Area in the years 2019-2022 were analyzed. Changes in the main accounting measures and market measures for individual countries as well as the characteristics of real estate market participants were analyzed.","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134956723","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}
Ahmad Akmal Isa, Muhammad Najib Razali, Fatin Afiqah Azmi, Siti Zaleha Daud, Aminah Mohsin, Azizah Ismail, Mohamad Amir Lokman
Abstract The COVID-19 pandemic has disrupted economies and industries worldwide, including the real estate sector. This study aims to assess the effects of the pandemic on commercial real estate prices in the Malaysian market. By examining variations in property types and considering key factors influencing pricing, the research contributes to a better understanding of the pandemic’s impact on the real estate market. To analyze the effects of the COVID-19 pandemic on commercial real estate prices, a mixed-method approach was employed. The study combines data from direct real estate indices, which provide insights into property prices based on transaction data, and listed real estate, which includes publicly traded real estate investment trusts (REITs). By utilizing both sources, a comprehensive analysis of the market is achieved. The sample for this study consists of commercial real estate properties in the Malaysian market. It includes properties from various sectors, such as retail, hospitality, and office buildings. The sample is representative of the overall market and captures the different property types affected by the pandemic. The analysis begins by comparing direct real estate indices to highlight the limitations and potential biases associated with using these indices. It then examines the variations in commercial real estate prices during the COVID-19 outbreak, focusing on the different property types. Statistical techniques, such as regression analysis and trend analysis, are employed to identify patterns and quantify the impact on commercial real estate prices. The study’s main findings reveal that the retail and hospitality sectors experienced the most significant impact on commercial real estate prices during the COVID-19 pandemic. These sectors witnessed a substantial decline in property values due to restrictions, lockdown measures, and reduced consumer demand. Office buildings, although moderately affected, also experienced some decline in prices. This research contributes to the existing literature on the effects of the COVID-19 pandemic on commercial real estate prices, specifically in the Malaysian market. By combining data from direct and listed real estate sources, the study provides a comprehensive understanding of the variations in property prices across different sectors. The findings offer valuable insights for real estate investors, policymakers, and industry professionals in adapting to the changing market conditions and making informed decisions regarding commercial real estate investments. In conclusion, this article sheds light on the effects of the COVID-19 pandemic on commercial real estate prices in the Malaysian market. The research methodology, which combines data from direct and listed real estate, allows for a comprehensive analysis of property variations among different sectors. The findings emphasize the significant impact on the retail and hospitality sectors, while showing office buildings to have been
{"title":"COVID-19 Impact to Retail, Hospitality, and Office Space in Malaysia","authors":"Ahmad Akmal Isa, Muhammad Najib Razali, Fatin Afiqah Azmi, Siti Zaleha Daud, Aminah Mohsin, Azizah Ismail, Mohamad Amir Lokman","doi":"10.2478/remav-2024-0008","DOIUrl":"https://doi.org/10.2478/remav-2024-0008","url":null,"abstract":"Abstract The COVID-19 pandemic has disrupted economies and industries worldwide, including the real estate sector. This study aims to assess the effects of the pandemic on commercial real estate prices in the Malaysian market. By examining variations in property types and considering key factors influencing pricing, the research contributes to a better understanding of the pandemic’s impact on the real estate market. To analyze the effects of the COVID-19 pandemic on commercial real estate prices, a mixed-method approach was employed. The study combines data from direct real estate indices, which provide insights into property prices based on transaction data, and listed real estate, which includes publicly traded real estate investment trusts (REITs). By utilizing both sources, a comprehensive analysis of the market is achieved. The sample for this study consists of commercial real estate properties in the Malaysian market. It includes properties from various sectors, such as retail, hospitality, and office buildings. The sample is representative of the overall market and captures the different property types affected by the pandemic. The analysis begins by comparing direct real estate indices to highlight the limitations and potential biases associated with using these indices. It then examines the variations in commercial real estate prices during the COVID-19 outbreak, focusing on the different property types. Statistical techniques, such as regression analysis and trend analysis, are employed to identify patterns and quantify the impact on commercial real estate prices. The study’s main findings reveal that the retail and hospitality sectors experienced the most significant impact on commercial real estate prices during the COVID-19 pandemic. These sectors witnessed a substantial decline in property values due to restrictions, lockdown measures, and reduced consumer demand. Office buildings, although moderately affected, also experienced some decline in prices. This research contributes to the existing literature on the effects of the COVID-19 pandemic on commercial real estate prices, specifically in the Malaysian market. By combining data from direct and listed real estate sources, the study provides a comprehensive understanding of the variations in property prices across different sectors. The findings offer valuable insights for real estate investors, policymakers, and industry professionals in adapting to the changing market conditions and making informed decisions regarding commercial real estate investments. In conclusion, this article sheds light on the effects of the COVID-19 pandemic on commercial real estate prices in the Malaysian market. The research methodology, which combines data from direct and listed real estate, allows for a comprehensive analysis of property variations among different sectors. The findings emphasize the significant impact on the retail and hospitality sectors, while showing office buildings to have been ","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135810195","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}
Abstract The paper deals with the conceptualization of the gross development value to assess direct damages and the restoration needs of lost, destroyed and damaged real estate as a result of armed aggression. A critical review of the existing practice of assessing property damage has been carried out. The measurement units of direct damages and the restoration needs, the evidence base used, the valuation methods for the determination of property damage are analyzed. The methodological potential of compounded cash flow models and the criteria for assessing their reliability is substantiated. A system of valuation models for calculating direct damages and restoration needs is proposed, depending on the category of real estate and market conditions at the valuation date. These valuation models are relatively simple to implement and understandable to the intended users of property damage valuation reports.
{"title":"Property Damage Assessment Methods and Models due to Armed Aggression","authors":"O. Drapikovskyi, Iryna Ivanova","doi":"10.2478/remav-2023-0019","DOIUrl":"https://doi.org/10.2478/remav-2023-0019","url":null,"abstract":"Abstract The paper deals with the conceptualization of the gross development value to assess direct damages and the restoration needs of lost, destroyed and damaged real estate as a result of armed aggression. A critical review of the existing practice of assessing property damage has been carried out. The measurement units of direct damages and the restoration needs, the evidence base used, the valuation methods for the determination of property damage are analyzed. The methodological potential of compounded cash flow models and the criteria for assessing their reliability is substantiated. A system of valuation models for calculating direct damages and restoration needs is proposed, depending on the category of real estate and market conditions at the valuation date. These valuation models are relatively simple to implement and understandable to the intended users of property damage valuation reports.","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":"31 1","pages":"32 - 43"},"PeriodicalIF":0.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44831158","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}
Abstract This article addresses the issue of the utility of money and the utility of housing with a value equivalent to that amount of money. The literature provides many reports on the shape of the utility function for money, but much less research has been devoted to the utility function for housing. The aim of this study was to estimate the utility function of money and housing according to the cumulative prospect theory (CPT) developed by Tversky and Kahneman (1992). Parameters alpha (α), beta (β), and lambda (λ) were estimated to compare the utility value of money and housing. The most important conclusions of the study are as follows: parameters alpha and beta were greater than 0 and less than 1 for both housing and money. Function v(x) was concave in the gain domain and convex in the loss domain, which is consistent with the CPT. The differences in the lambda parameter denoting loss aversion were not significant, and the value of the utility function was somewhat higher for money than for housing. This study was undertaken to estimate the CPT parameters for housing, which, according to the authors’ best knowledge, has not been investigated to date.
{"title":"Estimation of the Utility Function of Money and Housing Based on the Cumulative Prospect Theory","authors":"Justyna Brzezicka, M. Tomal","doi":"10.2478/remav-2023-0024","DOIUrl":"https://doi.org/10.2478/remav-2023-0024","url":null,"abstract":"Abstract This article addresses the issue of the utility of money and the utility of housing with a value equivalent to that amount of money. The literature provides many reports on the shape of the utility function for money, but much less research has been devoted to the utility function for housing. The aim of this study was to estimate the utility function of money and housing according to the cumulative prospect theory (CPT) developed by Tversky and Kahneman (1992). Parameters alpha (α), beta (β), and lambda (λ) were estimated to compare the utility value of money and housing. The most important conclusions of the study are as follows: parameters alpha and beta were greater than 0 and less than 1 for both housing and money. Function v(x) was concave in the gain domain and convex in the loss domain, which is consistent with the CPT. The differences in the lambda parameter denoting loss aversion were not significant, and the value of the utility function was somewhat higher for money than for housing. This study was undertaken to estimate the CPT parameters for housing, which, according to the authors’ best knowledge, has not been investigated to date.","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":"31 1","pages":"83 - 92"},"PeriodicalIF":0.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43503835","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}
Abstract The article attempts to explain market levels of housing prices by supplementing the set of typical objective explanatory variables with variables of behavioral background. The proposed explanatory variables reflect the anchoring effect of prices, understood as the acceptance by market participants of such price levels that are justified not only in terms of socio-economic factors, but also in levels entrenched in their minds. The purpose of the study is to show that the anchoring effect identified through behavioral economics can be generalized and applied to the market behavior of many market participants, and thus explain the weak correspondence between listed housing prices and their objective factors. The study covers 17 local real estate markets in Poland and employs econometric models built under slightly modified procedures of backward stepwise regression.
{"title":"The Effect of Price Anchoring on the Housing Market Based on Studies of Local Markets in Poland","authors":"S. Kokot","doi":"10.2478/remav-2023-0020","DOIUrl":"https://doi.org/10.2478/remav-2023-0020","url":null,"abstract":"Abstract The article attempts to explain market levels of housing prices by supplementing the set of typical objective explanatory variables with variables of behavioral background. The proposed explanatory variables reflect the anchoring effect of prices, understood as the acceptance by market participants of such price levels that are justified not only in terms of socio-economic factors, but also in levels entrenched in their minds. The purpose of the study is to show that the anchoring effect identified through behavioral economics can be generalized and applied to the market behavior of many market participants, and thus explain the weak correspondence between listed housing prices and their objective factors. The study covers 17 local real estate markets in Poland and employs econometric models built under slightly modified procedures of backward stepwise regression.","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":"31 1","pages":"44 - 57"},"PeriodicalIF":0.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45477371","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}
Abstract Real estate in the Guangdong-Hong Kong-Macao Greater Bay Area (also known as the Greater Bay Area, GBA) - a good representation of China’s advanced and developed urban agglomeration - has received considerable attention from the international community in recent years. However, the real estate market has been under extraordinary stress due to the expansion of COVID-19 in China, the strain on people’s livelihoods brought on by the coronavirus pandemic, and the Chinese government’s series of epidemic preventive initiatives. This study used a combination of qualitative and quantitative techniques, making use of interviews and questionnaires as instruments. It examined China’s GBA real estate market as the pandemic looms. The primary goals are to demonstrate the current state of the GBA’s real estate industry, pinpoint the factors holding back its growth, and estimate when the market might finally experience a breakthrough. Our findings suggested that the impact of COVID-19 on the GBA real estate sector in China is evident, but that it still has a bright future despite the negative externalities. This is because the city has a large population, high purchasing power, and is close to some of the most developed areas in southern China. This study establishes a baseline for studying the impact of China’s “One Belt, One Road” initiative on the GBA real estate market in the future. It also provides valuable resources for China’s GBA’s real estate industry.
{"title":"A Survey Analysis: The Current Real Estate Marketing Situation in the China Greater Bay Area in the Context of the COVID-19 Epidemic","authors":"Juan Kong, Ema Izati Zull Kepili","doi":"10.2478/remav-2023-0017","DOIUrl":"https://doi.org/10.2478/remav-2023-0017","url":null,"abstract":"Abstract Real estate in the Guangdong-Hong Kong-Macao Greater Bay Area (also known as the Greater Bay Area, GBA) - a good representation of China’s advanced and developed urban agglomeration - has received considerable attention from the international community in recent years. However, the real estate market has been under extraordinary stress due to the expansion of COVID-19 in China, the strain on people’s livelihoods brought on by the coronavirus pandemic, and the Chinese government’s series of epidemic preventive initiatives. This study used a combination of qualitative and quantitative techniques, making use of interviews and questionnaires as instruments. It examined China’s GBA real estate market as the pandemic looms. The primary goals are to demonstrate the current state of the GBA’s real estate industry, pinpoint the factors holding back its growth, and estimate when the market might finally experience a breakthrough. Our findings suggested that the impact of COVID-19 on the GBA real estate sector in China is evident, but that it still has a bright future despite the negative externalities. This is because the city has a large population, high purchasing power, and is close to some of the most developed areas in southern China. This study establishes a baseline for studying the impact of China’s “One Belt, One Road” initiative on the GBA real estate market in the future. It also provides valuable resources for China’s GBA’s real estate industry.","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":"31 1","pages":"1 - 19"},"PeriodicalIF":0.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46185223","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}
Abstract A huge effort has already been made to prove the existence of housing market segments, as well as how to utilize them to improve valuation accuracy and gain knowledge about the inner structure of the entire superior housing market. Accordingly, many different methods on the topic have been explored, but no universal framework is yet known. The aim of this article is to review some previous studies on data-driven housing market segmentation methods with a focus on clustering methods and their ability to capture market segments with respect to the shape of clusters, fuzziness and hierarchical structure.
{"title":"Review of Clustering Methods Used in Data-Driven Housing Market Segmentation","authors":"Štěpán Skovajsa","doi":"10.2478/remav-2023-0022","DOIUrl":"https://doi.org/10.2478/remav-2023-0022","url":null,"abstract":"Abstract A huge effort has already been made to prove the existence of housing market segments, as well as how to utilize them to improve valuation accuracy and gain knowledge about the inner structure of the entire superior housing market. Accordingly, many different methods on the topic have been explored, but no universal framework is yet known. The aim of this article is to review some previous studies on data-driven housing market segmentation methods with a focus on clustering methods and their ability to capture market segments with respect to the shape of clusters, fuzziness and hierarchical structure.","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":"31 1","pages":"67 - 74"},"PeriodicalIF":0.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44063712","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}
Abstract Abandoned houses have become a common feature of the local landscapes: the rising number of abandoned houses is a major challenge facing many counties in South Korea. Their presence negatively influences the neighborhood by undermining its aesthetic quality, depreciating the perception of safety in the neighborhood properties, and deepening the fiscal deficit of local financing. The detection of abandoned houses is the first step toward adequate housing management by local governments. This study aims to provide a cost-effective and prompt approach to identifying abandoned houses in rural areas. Multi-source data, that is, images and building registry data are utilized and a multi-input neural network is designed to adopt these heterogeneous datasets. Trained by the two source datasets, the proposed network achieves 86.2% accuracy in classifying abandoned houses, which is an acceptable performance level in administrative practice. The database of abandoned houses identified in this manner is expected to promote effective housing management by governments and ultimately contribute to mitigating vacancies in rural areas.
{"title":"Detecting Abandoned Houses in Rural Areas using Multi-Source Data","authors":"Chan-Jae Lee","doi":"10.2478/remav-2023-0021","DOIUrl":"https://doi.org/10.2478/remav-2023-0021","url":null,"abstract":"Abstract Abandoned houses have become a common feature of the local landscapes: the rising number of abandoned houses is a major challenge facing many counties in South Korea. Their presence negatively influences the neighborhood by undermining its aesthetic quality, depreciating the perception of safety in the neighborhood properties, and deepening the fiscal deficit of local financing. The detection of abandoned houses is the first step toward adequate housing management by local governments. This study aims to provide a cost-effective and prompt approach to identifying abandoned houses in rural areas. Multi-source data, that is, images and building registry data are utilized and a multi-input neural network is designed to adopt these heterogeneous datasets. Trained by the two source datasets, the proposed network achieves 86.2% accuracy in classifying abandoned houses, which is an acceptable performance level in administrative practice. The database of abandoned houses identified in this manner is expected to promote effective housing management by governments and ultimately contribute to mitigating vacancies in rural areas.","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":"31 1","pages":"58 - 66"},"PeriodicalIF":0.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43656310","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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