Abstract Real estate market analysis can involve many aspects. One of them is the study of the influence of various factors on prices and property values. For this type of issues, different kinds of measures and statistical models are often used. Many of them do not give unambiguous results. One of the reasons for this is the fact that the real estate market is characterized by the concept of local markets, which may be affected in different ways by economic, social, technical, environmental and other factors. Incorporating the influence of local markets, otherwise known as submarkets, into models often helps improve the precision of mass real estate valuation results. The delineation of submarket boundaries can be done in several different ways. One tool that is helpful in these types of situations are geographically weighted regression (GWR) models. The problem that may arise when using such models is related to the nature of some market factors, which may be of a qualitative nature. Because neighborhoods of individual properties may lack variability in terms of some variables, estimating GWR models is significantly difficult or impossible. The study will present an approach in which the categorical variables are transformed into a single synthetic variable, and only this variable will constitute the explanatory variable in the model. Areas where the slope parameters of the GWR model are similar were considered a submarket. The purpose of this paper is to determine the boundaries of submarkets in the study area and to compare the results of modeling the value of real estate using models that do not take local markets into account, as well as those that take into account local markets determined by experts and using the GWR model.
{"title":"Categorical Variable Problem In Real Estate Submarket Determination With Gwr Model","authors":"S. Gnat","doi":"10.2478/remav-2022-0028","DOIUrl":"https://doi.org/10.2478/remav-2022-0028","url":null,"abstract":"Abstract Real estate market analysis can involve many aspects. One of them is the study of the influence of various factors on prices and property values. For this type of issues, different kinds of measures and statistical models are often used. Many of them do not give unambiguous results. One of the reasons for this is the fact that the real estate market is characterized by the concept of local markets, which may be affected in different ways by economic, social, technical, environmental and other factors. Incorporating the influence of local markets, otherwise known as submarkets, into models often helps improve the precision of mass real estate valuation results. The delineation of submarket boundaries can be done in several different ways. One tool that is helpful in these types of situations are geographically weighted regression (GWR) models. The problem that may arise when using such models is related to the nature of some market factors, which may be of a qualitative nature. Because neighborhoods of individual properties may lack variability in terms of some variables, estimating GWR models is significantly difficult or impossible. The study will present an approach in which the categorical variables are transformed into a single synthetic variable, and only this variable will constitute the explanatory variable in the model. Areas where the slope parameters of the GWR model are similar were considered a submarket. The purpose of this paper is to determine the boundaries of submarkets in the study area and to compare the results of modeling the value of real estate using models that do not take local markets into account, as well as those that take into account local markets determined by experts and using the GWR model.","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":"30 1","pages":"42 - 54"},"PeriodicalIF":0.8,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42605135","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}
Małgorzata Rymarzak, E. Siemińska, Krzysztof Sakierski
Abstract The combination of policy concerns over climate and demographic change, energy shortages, resource efficiency and the natural environment, has led municipalities to be expected to reflect sustainability in different actions, including the decision-making on a considerable amount of their real property assets. As more and more municipalities, use the highest and best use analysis for reviewing the configuration of real property asset portfolio to achieve public goals, this provokes an examination of the reflection of sustainability (environmental, economic and social dimensions) in this kind of elaboration. Thus, this paper aims to investigate how Polish municipalities deal with the incorporation of sustainability into the highest and best use analysis and its operationalization in four tests (legally permissible, physically possible, financially feasible, and maximally productive). The research goal was pursued based on quantitative research using surveys conducted between April and May 2022 among eleven municipalities (creating the largest metropolitan areas in Poland) and qualitative research by the content analysis of HBU analyses prepared for them in previous years.
{"title":"Reflecting Sustainability in the Analysis of Highest and Best Use: Evidence from Polish Municipalities","authors":"Małgorzata Rymarzak, E. Siemińska, Krzysztof Sakierski","doi":"10.2478/remav-2022-0032","DOIUrl":"https://doi.org/10.2478/remav-2022-0032","url":null,"abstract":"Abstract The combination of policy concerns over climate and demographic change, energy shortages, resource efficiency and the natural environment, has led municipalities to be expected to reflect sustainability in different actions, including the decision-making on a considerable amount of their real property assets. As more and more municipalities, use the highest and best use analysis for reviewing the configuration of real property asset portfolio to achieve public goals, this provokes an examination of the reflection of sustainability (environmental, economic and social dimensions) in this kind of elaboration. Thus, this paper aims to investigate how Polish municipalities deal with the incorporation of sustainability into the highest and best use analysis and its operationalization in four tests (legally permissible, physically possible, financially feasible, and maximally productive). The research goal was pursued based on quantitative research using surveys conducted between April and May 2022 among eleven municipalities (creating the largest metropolitan areas in Poland) and qualitative research by the content analysis of HBU analyses prepared for them in previous years.","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":"30 1","pages":"103 - 115"},"PeriodicalIF":0.8,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46126844","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}
O. Melnychenko, T. Osadcha, A. Kovalyov, V. Matskul
Abstract The study aimed to examine the impact of inflation on the real estate market using Polish panel data for the last 13 years. It is based on a panel model, where price changes of one square meter of housing are determined as a function in changes of inflation, the central bank’s base rate, dwellings built, as well as new mortgage loans. The quarterly dynamics of the average price of 1 square meter of housing in Poland’s eight largest cities in the 2009-2021 period was studied. This price was modeled and predicted using one of the Box-Jenkins time series models: the Holt-Winter model of exponential smoothing with a damped trend. The forecasting results showed a small (up to 4%) relative error in comparison with the actual data. In addition, the moment (2017) of the price trend change was found. Therefore, piecewise linear regressions with high regression coefficients were used when modeling the impact of inflation changes on the real estate market indicators under consideration. The results obtained provide valuable insight into the relationship of real estate market indicators, allowing consumers to predict available options and make decisions in accordance with their preferences.
{"title":"Dependence of Housing Real Estate Prices on Inflation as One of the Most Important Factors: Poland’s Case","authors":"O. Melnychenko, T. Osadcha, A. Kovalyov, V. Matskul","doi":"10.2478/remav-2022-0027","DOIUrl":"https://doi.org/10.2478/remav-2022-0027","url":null,"abstract":"Abstract The study aimed to examine the impact of inflation on the real estate market using Polish panel data for the last 13 years. It is based on a panel model, where price changes of one square meter of housing are determined as a function in changes of inflation, the central bank’s base rate, dwellings built, as well as new mortgage loans. The quarterly dynamics of the average price of 1 square meter of housing in Poland’s eight largest cities in the 2009-2021 period was studied. This price was modeled and predicted using one of the Box-Jenkins time series models: the Holt-Winter model of exponential smoothing with a damped trend. The forecasting results showed a small (up to 4%) relative error in comparison with the actual data. In addition, the moment (2017) of the price trend change was found. Therefore, piecewise linear regressions with high regression coefficients were used when modeling the impact of inflation changes on the real estate market indicators under consideration. The results obtained provide valuable insight into the relationship of real estate market indicators, allowing consumers to predict available options and make decisions in accordance with their preferences.","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":"30 1","pages":"25 - 41"},"PeriodicalIF":0.8,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42677630","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 In this paper, we studied the influence of interest rates on a US-based real estate private equity index as well US Wilshire public equity REIT Index. The interest rates that are chosen as independent variables include Monthly LIBOR, Yearly LIBOR and the Federal Cost of Funds Index. The dependent variables include US-based real estate private equity index that includes quarterly returns of 1,035 real estate funds, including liquidated funds formed between 1986 and 2018. The other dependent variable is the US Wilshire REIT Index. The variance of returns of interest rates considerably influences the variance of returns of the US PERE Index, whereas variance of returns of interest rates doesn’t influence the variance of returns of the US Wilshire REIT Index. Also, the real estate index is positively correlated to interest rates and so rising interest rates influence the returns of US PERE Index in a positive manner. The study shows that private equity real estate investors should expect higher return as the cost of funds increase.
{"title":"Comparative Analysis: Influence of Interest Rates on Returns of Real Estate Private Equity Index and Real Estate Public Equity Index","authors":"M. Sharma","doi":"10.2478/remav-2022-0026","DOIUrl":"https://doi.org/10.2478/remav-2022-0026","url":null,"abstract":"Abstract In this paper, we studied the influence of interest rates on a US-based real estate private equity index as well US Wilshire public equity REIT Index. The interest rates that are chosen as independent variables include Monthly LIBOR, Yearly LIBOR and the Federal Cost of Funds Index. The dependent variables include US-based real estate private equity index that includes quarterly returns of 1,035 real estate funds, including liquidated funds formed between 1986 and 2018. The other dependent variable is the US Wilshire REIT Index. The variance of returns of interest rates considerably influences the variance of returns of the US PERE Index, whereas variance of returns of interest rates doesn’t influence the variance of returns of the US Wilshire REIT Index. Also, the real estate index is positively correlated to interest rates and so rising interest rates influence the returns of US PERE Index in a positive manner. The study shows that private equity real estate investors should expect higher return as the cost of funds increase.","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":"30 1","pages":"17 - 24"},"PeriodicalIF":0.8,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44865720","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 Turkish Housing Market has experienced a steep increase in prices. Individual and corporate investors now possess tools to estimate the real estate evaluation while using smaller amounts of data with traditional techniques. Not having an analytical approach to evaluate the price of real estate could cause the investor to lose considerable amounts of money, especially in the case of individual investors. This study aims to determine how different machine learning algorithms with real market data can improve this process. To be able to test this, over 30000 lines of housing market data with over 13 variables is scraped. Data is cleansed, manipulated and visualized, while predictive models such as linear regression, polynomial regression, decision trees, random forests, and XGboost are created and compared according to the CRISP-DM framework. The results show that using complex techniques to create machine learning models could improve the accuracy in predicting the listing prices of houses. This paper aims to: – analyze the effects of using a real and relatively large amount of data, – determine the main variables that contribute to the evaluation of an estate, – compare different machine learning models to find the optimal one for the real estate market, – create an accurate model to predict the value of any house on the Istanbul market.
{"title":"Real Estate Market Price Prediction Model of Istanbul","authors":"Mert Tekin, I. Sari","doi":"10.2478/remav-2022-0025","DOIUrl":"https://doi.org/10.2478/remav-2022-0025","url":null,"abstract":"Abstract The Turkish Housing Market has experienced a steep increase in prices. Individual and corporate investors now possess tools to estimate the real estate evaluation while using smaller amounts of data with traditional techniques. Not having an analytical approach to evaluate the price of real estate could cause the investor to lose considerable amounts of money, especially in the case of individual investors. This study aims to determine how different machine learning algorithms with real market data can improve this process. To be able to test this, over 30000 lines of housing market data with over 13 variables is scraped. Data is cleansed, manipulated and visualized, while predictive models such as linear regression, polynomial regression, decision trees, random forests, and XGboost are created and compared according to the CRISP-DM framework. The results show that using complex techniques to create machine learning models could improve the accuracy in predicting the listing prices of houses. This paper aims to: – analyze the effects of using a real and relatively large amount of data, – determine the main variables that contribute to the evaluation of an estate, – compare different machine learning models to find the optimal one for the real estate market, – create an accurate model to predict the value of any house on the Istanbul market.","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":"30 1","pages":"1 - 16"},"PeriodicalIF":0.8,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41965273","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 study used Google search query data on real estate interest for several countries in the Baltic area. The dynamics of public interest in housing have been compared to the dynamics of the COVID-19 infections in Lithuania, Latvia, Poland, and Sweden. This study uses the Vector autoregressive (VAR) model to forecast such time series. VAR is a multivariate linear time series model in which the endogenous variables in the system are lagged functions of the values of all endogenous variables. The increase in COVID-19 infections negatively affected society’s interest in housing. The study used Google Trends and R software.
{"title":"Analysis of the Relationship Between COVID-19 Infections and Web-Based Housing Searches","authors":"M. Bełej","doi":"10.2478/remav-2022-0031","DOIUrl":"https://doi.org/10.2478/remav-2022-0031","url":null,"abstract":"Abstract The study used Google search query data on real estate interest for several countries in the Baltic area. The dynamics of public interest in housing have been compared to the dynamics of the COVID-19 infections in Lithuania, Latvia, Poland, and Sweden. This study uses the Vector autoregressive (VAR) model to forecast such time series. VAR is a multivariate linear time series model in which the endogenous variables in the system are lagged functions of the values of all endogenous variables. The increase in COVID-19 infections negatively affected society’s interest in housing. The study used Google Trends and R software.","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":"30 1","pages":"89 - 102"},"PeriodicalIF":0.8,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44253270","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 purpose of this paper is to estimate econometric models with sample and prior information. Prices of land property for residential development in Szczecin are modeled (the price level was determined for 2018). Modeling property prices only based on sample data generates numerous problems. Transaction databases from local real estate markets often contain a small number of observations. Properties are frequently similar, which results in low variability of property characteristics, and thus – low efficiency of parameter estimators. In such a situation, the impact of some features cannot be estimated from the sample data. As a solution to this problem, the paper proposes econometric models that consider prior information. This information can be, for example, in the form of property feature weights proposed by experts. The prior information will be expressed in the form of stochastic restrictions imposed on the model parameters. In the simulation experiment, the predictive power of mixed estimation models is compared with two kind of models: OLS models and model with only prior information. It turned out that mixed estimation results are superior with regard to formal criteria and predictive abilities.
{"title":"Econometric Models of Real Estate Prices with Prior Information. Mixed Estimation","authors":"M. Doszyń","doi":"10.2478/remav-2022-0021","DOIUrl":"https://doi.org/10.2478/remav-2022-0021","url":null,"abstract":"Abstract The purpose of this paper is to estimate econometric models with sample and prior information. Prices of land property for residential development in Szczecin are modeled (the price level was determined for 2018). Modeling property prices only based on sample data generates numerous problems. Transaction databases from local real estate markets often contain a small number of observations. Properties are frequently similar, which results in low variability of property characteristics, and thus – low efficiency of parameter estimators. In such a situation, the impact of some features cannot be estimated from the sample data. As a solution to this problem, the paper proposes econometric models that consider prior information. This information can be, for example, in the form of property feature weights proposed by experts. The prior information will be expressed in the form of stochastic restrictions imposed on the model parameters. In the simulation experiment, the predictive power of mixed estimation models is compared with two kind of models: OLS models and model with only prior information. It turned out that mixed estimation results are superior with regard to formal criteria and predictive abilities.","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":" ","pages":"61 - 72"},"PeriodicalIF":0.8,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44082233","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 asset pricing theory introduced by Fama and French (2015) documents five systematic common risk factors for equity valuation, such as: (a) market beta, (b) firm size, (c) firm value, (d) profitability and (e) investment strategy. However, corporate finance literature does not provide us with a particularly robust check if the FF5 model is equally exposed to estimate equity returns in an emerging market. Hence, based on Fama and Macbeth (1973) as well as Fama and French (1993, 2015, 2020), this paper applies multivariate regression (time series & cross-sectional) analysis for the robust test of common risk factors and risk premia respectively in an emerging market context, and finally validates that all of the systematic risk factors are significant except firm profitability and investment strategy. We found that the distinguishing semi-strong level of market efficiency influences the explanatory power of the underlying risk exposure for stock return performance differently in an emerging market. The finding could be important in estimating equity fair pricing that is yet to be examined for an emerging market. Therefore, with the reconfirmedthree significant common risk factors, the market practitioners, policy makers, financial analysts, and, above all, investors can estimate equity value appropriately, and thereby take optimal financial and investment decisions.
Fama和French(2015)提出的资产定价理论记录了股票估值的五个系统性常见风险因素，如:(a)市场贝塔系数，(b)公司规模，(c)公司价值，(d)盈利能力和(e)投资策略。然而，企业金融文献并没有为我们提供一个特别强大的检查，如果FF5模型同样暴露于新兴市场的估计股权回报。因此，本文基于Fama and Macbeth(1973)以及Fama and French(1993, 2015, 2020)，分别运用多元回归(时间序列&横截面)分析对新兴市场背景下的常见风险因素和风险收益进行稳健性检验，最终验证了除企业盈利能力和投资策略外，所有系统性风险因素均显著。我们发现，在新兴市场中，显著的半强市场效率水平对潜在风险敞口对股票收益表现的解释能力有不同的影响。这一发现可能对估计新兴市场的股票公平定价具有重要意义。因此，通过重新确认三个重要的共同风险因素，市场从业者、政策制定者、金融分析师，尤其是投资者可以适当地估计股权价值，从而做出最优的财务和投资决策。
{"title":"Asset Pricing Puzzle: New Evidence of Fama-French Five-Factors in Emerging Market Perspectives","authors":"M. Hossain","doi":"10.2478/remav-2022-0022","DOIUrl":"https://doi.org/10.2478/remav-2022-0022","url":null,"abstract":"Abstract The asset pricing theory introduced by Fama and French (2015) documents five systematic common risk factors for equity valuation, such as: (a) market beta, (b) firm size, (c) firm value, (d) profitability and (e) investment strategy. However, corporate finance literature does not provide us with a particularly robust check if the FF5 model is equally exposed to estimate equity returns in an emerging market. Hence, based on Fama and Macbeth (1973) as well as Fama and French (1993, 2015, 2020), this paper applies multivariate regression (time series & cross-sectional) analysis for the robust test of common risk factors and risk premia respectively in an emerging market context, and finally validates that all of the systematic risk factors are significant except firm profitability and investment strategy. We found that the distinguishing semi-strong level of market efficiency influences the explanatory power of the underlying risk exposure for stock return performance differently in an emerging market. The finding could be important in estimating equity fair pricing that is yet to be examined for an emerging market. Therefore, with the reconfirmedthree significant common risk factors, the market practitioners, policy makers, financial analysts, and, above all, investors can estimate equity value appropriately, and thereby take optimal financial and investment decisions.","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":"30 1","pages":"73 - 85"},"PeriodicalIF":0.8,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42678200","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}
О. Bochko, N. Kosar, N. Kuzo, I. Bilyk, O. Zarichna
Abstract The work presents an analysis of the housing construction market in Ukraine. An economic and mathematic model was built to determine that the growth of the volume of housing construction in Ukraine had a positive impact on its GDP, due of a close relation between the two values. It is important to identify factors influencing the volume of housing construction. The obtained results prove that the greatest impact is made by consumer income, deposit rates in foreign currency, and the amount of consumer loans for buying, building and reconstruction of real estate assets; the numbers of marriages, investments in housing construction and interest rates for mortgage credits in UAH also have a significant impact. The elasticity coefficients reveal a positive impact of such factors as an increase of consumer income, growth of investments in housing construction, reduction of interest rates for mortgage credits and deposit rates in foreign currency, reduction of the amount of consumer loans for buying, building and reconstruction of real estate assets, and reduction of the number of marriages. Further development of the housing construction market requires appropriate conditions for the development of the banking sector in Ukraine and the growth of investments in the studied industry.
{"title":"Determinants of Housing Construction in Ukraine","authors":"О. Bochko, N. Kosar, N. Kuzo, I. Bilyk, O. Zarichna","doi":"10.2478/remav-2022-0017","DOIUrl":"https://doi.org/10.2478/remav-2022-0017","url":null,"abstract":"Abstract The work presents an analysis of the housing construction market in Ukraine. An economic and mathematic model was built to determine that the growth of the volume of housing construction in Ukraine had a positive impact on its GDP, due of a close relation between the two values. It is important to identify factors influencing the volume of housing construction. The obtained results prove that the greatest impact is made by consumer income, deposit rates in foreign currency, and the amount of consumer loans for buying, building and reconstruction of real estate assets; the numbers of marriages, investments in housing construction and interest rates for mortgage credits in UAH also have a significant impact. The elasticity coefficients reveal a positive impact of such factors as an increase of consumer income, growth of investments in housing construction, reduction of interest rates for mortgage credits and deposit rates in foreign currency, reduction of the amount of consumer loans for buying, building and reconstruction of real estate assets, and reduction of the number of marriages. Further development of the housing construction market requires appropriate conditions for the development of the banking sector in Ukraine and the growth of investments in the studied industry.","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":" ","pages":"1 - 11"},"PeriodicalIF":0.8,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43061254","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 purpose of this study is to identify regularities in the price relations between primary and secondary housing markets. The primary market and the secondary market are two related but quite differentiated sub-segments of the residential market. They particularly differ in the qualitative features of their traded objects and, consequently, also in the prices recorded in their trading. Nevertheless, they remain under the influence of the same main factors of a macroeconomic nature. This gives rise to the research hypothesis that prices of flats quoted in the sub-segments of the residential market remain in specific relationships with one another. In an attempt to verify this hypothesis, the paper presents the results of an analytical work on the search for regularities in the relationship between prices on primary and secondary housing markets in selected Polish cities. The regularities concern the dynamics and structure of price relation indices constructed for the research. They also include classification analyses. The findings of the research have revealed, inter alia, that in the majority of the cities under study, prices of flats in the primary markets are higher than prices in the secondary markets. However, situations in which the reverse happens periodically (sometimes occasionally) are not rare. The examined relations are not permanent and are subject to relatively large, irregular fluctuations over time. It is possible to distinguish groups of cities which are relatively similar in this respect, but these similarities are not strong.
{"title":"Identification of Regularities in Relation Between Prices on Primary and Secondary Housing Market in Selected Cities in Poland","authors":"S. Kokot","doi":"10.2478/remav-2022-0020","DOIUrl":"https://doi.org/10.2478/remav-2022-0020","url":null,"abstract":"Abstract The purpose of this study is to identify regularities in the price relations between primary and secondary housing markets. The primary market and the secondary market are two related but quite differentiated sub-segments of the residential market. They particularly differ in the qualitative features of their traded objects and, consequently, also in the prices recorded in their trading. Nevertheless, they remain under the influence of the same main factors of a macroeconomic nature. This gives rise to the research hypothesis that prices of flats quoted in the sub-segments of the residential market remain in specific relationships with one another. In an attempt to verify this hypothesis, the paper presents the results of an analytical work on the search for regularities in the relationship between prices on primary and secondary housing markets in selected Polish cities. The regularities concern the dynamics and structure of price relation indices constructed for the research. They also include classification analyses. The findings of the research have revealed, inter alia, that in the majority of the cities under study, prices of flats in the primary markets are higher than prices in the secondary markets. However, situations in which the reverse happens periodically (sometimes occasionally) are not rare. The examined relations are not permanent and are subject to relatively large, irregular fluctuations over time. It is possible to distinguish groups of cities which are relatively similar in this respect, but these similarities are not strong.","PeriodicalId":37812,"journal":{"name":"Real Estate Management and Valuation","volume":"30 1","pages":"45 - 60"},"PeriodicalIF":0.8,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45773486","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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