Volume 30 (2022): Issue 4 (December 2022)

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.

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.

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.

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.

The airport may be an opportunity for the development of airport-proximate areas, as well as a source of conflicts and nuisances for stakeholders. From the perspective of spatial order and sustainable development, it is necessary to create a coherent vision of the development and operationalize it via spatial management. This article aims to analyze spatial management in areas proximate to Gdansk Airport in the context of spatial chaos. The analyses are based on 232 local spatial development plans for the period 1996-2020, for 11 selected areas in the vicinity of the Gdansk Lech Walesa Airport, documents obtained from the local government, and open-source data. The research concentrates on the analysis of the functions of areas, spatial chaos, and the threat of potential conflicts. The results demonstrate the spatial chaos in proximate areas of Gdansk Airport. This implies that the decisions made by the authorities responsible for spatial management do not respect spatial order and sustainable development and contribute to spatial chaos.

There have been extensive studies pertaining on bubble detection in literature, though very few investigate the Malaysian residential property market. The inflated housing market, however, has sparked widespread public anxiety and there has been a proliferation of comments and forecasts about the presence of housing bubbles in Malaysia throughout the last decade. The purpose of this paper is to assess the housing bubbles in Malaysia by using empirical models in detecting Malaysian residential property bubbles. This research employed the Markov Switching (MS) model to investigate the housing bubbles for the Malaysian residential property market. The findings revealed Malaysian housing prices to be relatively stable over the period 2010 to 2019, with states of upheaval occurring only during short-lived periods. Overall, Malaysian housing prices were generally steady between 2010 and 2019, albeit this has shifted slowly in recent years as economic turmoil faded. This study provides empirical results to explain the situation of Malaysian house prices in the recent years.

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.

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.