Real Estate Prices Research Articles

A Study on the Forecasting of Real Estate Market Using Algorithms

Technology using algorithms has an impact on various aspects of the real estate market, such as real estate market price prediction, investment recommendation, and rent setting. First, it was confirmed that by improving the accuracy of real estate price prediction, real estate market price prediction using AI algorithm can derive more accurate results through machine learning and deep learning technologies based on past data. In addition, it is the development of an investment recommendation system. The investment recommendation system using AI can promote efficient fund management by discovering promising investment opportunities in the real estate market and proposing them to investors. The next is the optimization of lease on a deposit basis, monthly rent, and rent setting. The rent setting model using AI technology can set the rent appropriately through negotiations between real estate owners and tenants in consideration of market trends, demand, and predictions by region. Finally, it is the automation and efficiency of the real estate transaction process system. Smart contracts and automation systems using AI can save money and time between transaction parties by simplifying and streamlining the real estate transaction process. However, problems with data security and privacy protection are emerging as new challenges. New security and privacy problems arise in dealing with personal information, transactions, and data, and appropriate responses to them are urgent.

Application of Feature Engineering Techniques and Machine Learning Algorithms for Property Price Prediction

This research applies feature engineering techniques and machine learning algorithms to predict property prices using a dataset from Kaggle. Three models were implemented: Linear Regression, Decision Tree, and Random Forest. The Random Forest model demonstrated the best performance with an average Mean Absolute Error (MAE) of 16472.76, Mean Squared Error (MSE) of 457407807.78, and R-squared (R²) of 0.83. Key features influencing property prices were identified through feature importance analysis, providing valuable insights for enhancing property appraisals and investment decisions.

How permitting process length influences development costs and real estate prices

How permitting process length influences development costs and real estate prices

Simulating urban energy use under climate change scenarios and retrofit plans in coastal Texas

Rapid urbanization, climate change, and aging infrastructure pose significant challenges to achieving sustainability and resilience goals in urban building energy use. Although retrofitting offers a viable solution to mitigate building energy use, there has been limited analysis of its effects under various weather conditions associated with climate change in urban building energy use simulations. Moreover, certain parameters in energy simulations necessitate extensive auditing or survey work, which is often impractical. This research proposes a framework that integrates various datasets, including building footprints, Lidar data, property appraisals, and street view images, to conduct neighborhood-scale building energy use analysis using the Urban Modeling Interface (UMI), an Urban Building Energy Model (UBEM), in a coastal neighborhood in Galveston, Texas. Seven retrofit plans and three weather conditions are considered in the scenarios of building energy use. The results show that decreasing the U-value of building envelopes helps reduce energy use, while increasing the U-value leads to higher energy consumption in the Galveston neighborhood. This finding provides direction for coastal Texas cities, like Galveston, to update building standards and implement retrofit measures.

Multilevel modelling, location and property valuations: an application to the Italian residential market

PurposeThe use of statistical methods in the field of real estate appraisals presents a trade-off between the efficiency of the estimates (that would require the use of sophisticate econometric models) and the ease of the economic interpretation of the outcomes (that characterises the hedonic pricing models). This paper shows that multilevel modelling (MLM) can represent a suitable solution to this trade-off.Design/methodology/approachThis paper uses the so-called “multilevel modelling” (henceforth MLM). MLM can represent a further step forward in the use of statistical methods in property appraisals. MLM is easy to implement and the MLM estimates have a clear economic meaning. Furthermore, MLM provides more efficient estimates of hedonic prices (the prices of housing attributes) with respect to standard hedonic pricing models. Finally, MLM is particularly suitable for the housing market analysis, where the feature “location” plays a key role.FindingsFor the Italian context, characterised by many “benchmark locations” (small municipalities that share similar geographic, historic, and socioeconomic characteristics), the paper finds that multilevel modelling (MLM) is needed to correctly estimate the hedonic prices also in a micro-area.Practical implicationsMLM allows to further enhance the key role of “location”. Location is indeed used as the “grouping variable” in MLM, instead of being treated as a generic housing attribute in hedonic pricing models. When the benchmark locations are many, therefore, MLM represents a very effective compromise between the estimates’ efficiency and the ease of outcomes’ economic interpretation.Originality/valueUnlike the related literature that, basically, use MLM to investigate what are the main determinants (levels) of housing prices, this paper uses MLM to make more efficient the estimation of hedonic prices.

Determinants of housing prices: Serbian Cities’ perspective

This study investigates the spatial and temporal dynamics of housing prices in Serbia, addressing the critical need to understand the drivers of real estate prices and their implications for economic and social welfare. Employing a panel data analysis approach on a unique dataset covering 24 distinct urban areas in Serbia from 2011 to 2021, we examine the relevance of diverse economic, demographic, and infrastructural indicators, providing novel insights within a developing country context. Our findings reveal that the housing market stock-flow model effectively predicts housing price appreciation trends, explaining over 60 percent of variation in property prices. Notably, disparities in labour income, captured by average wages and registered employment rates, emerge as significant determinants of real estate prices, underlining socio-economic disparities within Serbian cities. Housing prices exhibit a positive response to the population/housing stock ratio, suggesting higher prices in cities experiencing faster population growth relative to housing supply. Intensified construction is associated with elevated housing prices. Additionally, we find positive association between the inflation variable and housing prices, underlining real estate’s potential as an inflation hedge. Public service provision and infrastructural amenities also emerge as contributors to higher housing prices in urban areas, emphasizing the importance of comprehensive urban planning strategies. Our study contributes to the literature by providing specific quantitative evidence, advancing the understanding of urban housing market dynamics in developing countries. By offering nuanced insights into determinants of housing prices, our research informs policymakers and urban planners seeking to foster equitable and sustainable urban development strategies.

Do housing prices decrease when interest rates go up? Housing prices and interest rates: understanding the positive correlation across five countries

This paper investigates the relationship between changes in interest rate and house prices from the period 1975 to 2023 in five countries: United States, United Kingdom, China, Germany and Indonesia. Results show that contrary to the paradigm of real estate pricing, housing prices increase with interest rates. Furthermore we find that changes in house prices are independent of monetary policies and interest rate changes set by the central bank. Economically developed nations exhibit a stronger correlation between the two variables. We demonstrate that this is due to the 2008 financial crisis, where houses fell by nearly 30%, creating a fear of recession and the resulting policy of low interest rates to encourage investment.

The effect of a multi-school zoning policy on the capitalization of educational resources: A quasi-natural experiment in Beijing

The problem of capitalizing high-quality education resources into real estate prices has been widely analyzed. This paper discusses the influence of school district policy—the “multi-school zoning (MSZ)” policy—on district housing prices in Beijing. The MSZ policy randomly assigns students to key primary schools (A public primary school with a long history and outstanding teaching quality), indirectly affecting residents' house-buying behavior. Using data from 60,663 housing transactions, we evaluated the effect of the policy through regression discontinuity (RD) design methodology. The results showed that: (1) overall house prices decreased by 8.2 % in Xicheng, and by 10.1 % in Haidian. The more significant decrease in Haidian is likely because of the large differences in educational resources in that area. (2) Housing prices in key vs. high-quality school districts in Xicheng decreased by 10.7 % and 10.6 %, respectively, likely because of the superior quality and balanced educational resources in that region. The effect of the MSZ policy on housing prices in key and high-quality school districts is similar. (3) Housing prices in different school districts in Haidian decreased by 12.5 % and 6.8 %, respectively. The unique merit-based enrollment model of Haidian undermines the random allocation aspect of the MSZ policy, thereby diminishing its inhibitory effect on housing prices in high - quality school districts. These findings shed light on the MSZ's role in preventing the capitalization of educational resources, which varies among school districts. The results may serve as a reference for policy makers in subsequent implementations.

Identifying the Current Status of Real Estate Appraisal Methods

Identifying the Current Status of Real Estate Appraisal Methods

Pre-owned housing price index forecasts using Gaussian process regressions

Purpose The purpose of this study is to make property price forecasts for the Chinese housing market that has grown rapidly in the last 10 years, which is an important concern for both government and investors. Design/methodology/approach This study examines Gaussian process regressions with different kernels and basis functions for monthly pre-owned housing price index estimates for ten major Chinese cities from March 2012 to May 2020. The authors do this by using Bayesian optimizations and cross-validation. Findings The ten price indices from June 2019 to May 2020 are accurately predicted out-of-sample by the established models, which have relative root mean square errors ranging from 0.0458% to 0.3035% and correlation coefficients ranging from 93.9160% to 99.9653%. Originality/value The results might be applied separately or in conjunction with other forecasts to develop hypotheses regarding the patterns in the pre-owned residential real estate price index and conduct further policy research.

The Use of Rate Study in Real Estate Market Analysis within the Mass Appraisal System in the Republic of Moldova

This article investigates issues related to determining real estate market indicators in the framework of mass appraisal of immovable property in the Republic of Moldova, according to the recent methodological framework issued by the authorities, set to be implemented in practice. Simultaneously, this study proposes an applied methodology for real estate market analysis, aiming to identify specific indicators necessary for an adequate and equitable assessment of the real estate market at both regional and national levels. The article systematically synthesizes the limitations associated with using arithmetic mean to calculate market prices per unit as market indicators, and highlights the advantages of rate study as an alternative for monitoring and analysing the real estate market. The effectiveness of the proposed methodology is demonstrated through a case study, arguing for its necessity in practice.

Impact Analysis of the Law on Diminishing Returns on the Mass Appraisal of Real Estate in the Republic of Moldova

The present study investigates the shortcomings of the linear models of massive evaluation of real estate in the Republic of Moldova, currently used to determine the taxable value, the author noting, in particular, the influence of the distortion of the calculated values, within the mathematical modeling and the need to respect the law of diminishing returns. The challenges are relevant for countries with an ad-valorem taxation system, such as the Republic of Moldova, which needs to harmonize its national legislation with the criteria for joining the European Union. The study uses the system of mass evaluation of real estate properties in order to identify and develop optimization solutions for the massive evaluation system of real estate properties, in order to tax them, by implementing log-linear models, with effective results compared to existing linear regressions. By benchmarking performance metrics between linear regressions from existing and refined models, the research improves the taxation system through more accurate methodologies.

Standardization of Regression Equation Parameters in the Case of Multiple Linear Regression for an Econometric Model Development to Determine the Price of Apartments

Abstract This study examines the relationship between apartment prices in Romania and various influencing factors, for which we have constructed a model using multiple linear regression. This type of regression equation's parameters hold practical significance as they aid in establishing the regression equation. The form of the connection, specifically the parameters that define it, greatly influences our understanding of the connection between various quantified variables in the proposed model. By determining the regression function in this manner, we created a model that allows us to make predictions or draw conclusions based on the variables within the model. Microeconomic variables are crucial in influencing real estate price variations at the individual property level. These variables are more specific and relate to factors within the local market of Arad or the property itself. Some key microeconomic variables can influence real estate prices, which are important to ascertain using statistical tools.

Real estate price forecasting utilizing recurrent neural networks incorporating genetic algorithms

This study aims to develop and examine the effectiveness of the Recurrent Neural Network (RNN) model incorporating Genetic Algorithm (GA) in forecasting real estate prices. Real estate prices have a significant impact on a country’s financial system. Therefore, the ability to accurately forecast its price is valuable. A set of data containing 5.4 million unique records of real estate with their prices is used in the study. The data set, which spans from 2018 to 2021, contains twelve independent variables and one dependent variable. We preprocessed the data set to reduce noise and outliers that could potentially lead to poor model performance. The RNN model was selected because (GA) optimises the hyperparameters in the Recurrent Neural Network (RNN) hidden layer to optimise the performance of the RNN model. Relative Root Mean Squared Error (RRMSE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) examine the effectiveness of the RNN-GA model.The results show that the RNN-GA outperforms RNN and the traditional statistical models used in the real estate industry. This study provides an understanding of the effectiveness of incorporating GA in optimizing the RNN model for real estate price forecasting, which could benefit stakeholders in the real estate industry and sustain the financial system. This study uses a novel hybrid RNN-GA model for predicting housing prices with a large dataset.

Role of Monetary Policy in Asset Price Inflation: A Comparative Study of Developed and Developing Economies

This study examined the relationship between monetary policy and asset price inflation in developed and developing economies, focusing on Pakistan. Using data from 2010-2024, the research employed multivariate GARCH and causality models to analyze the impact of various monetary policy tools on stock market indices and real estate prices. The findings revealed significant differences in economic policy's transmission mechanisms and effectiveness in influencing asset prices between developed and developing economies. In Pakistan, interest rate changes were found to have a more pronounced effect on stock market volatility than real estate prices. The study also highlighted the challenges developing economies face in implementing effective monetary policies to manage asset price inflation without compromising economic growth. This research is particularly important as it provides crucial insights for policymakers in developing economies like Pakistan, where the interplay between monetary policy and asset prices can have significant implications for financial stability and economic growth. By identifying the asymmetric effects of monetary policy and the role of policy uncertainty, this study contributes to developing more effective and targeted policy interventions in emerging markets.

Wetland gentrification: the African variant on ecological gentrification

There is growing scholarly interest in notions of ecological gentrification – whereby environmental improvements drive up real estate prices, with the subsequent displacement of low-income residents – particularly in the global North. Drawing on an integrative review of extant literature on African urbanism, urban greenery encroachment and gentrification, this paper argues that the Western notion of ecological gentrification has little or no application in African cities, which are confronted with rapid encroachment on urban wetlands amidst burgeoning urbanization and its attendant increasing demand for property development. We conceptualize the African variant on ecological gentrification as “wetland gentrification”. It occurs when land scarcity and rising property values in African cities cause indiscriminate depletion of urban wetlands for property development, leading to the destruction of ecological resources and displacement of human populations. This paper discusses, among other topics, the implications of wetland gentrification for urban governance and vice versa.

Determinants of Residential Real Estate Property Prices in Ethiopia: The Case of Bahir Dar City

The Ethiopian real estate industry has grown in the previous two decades, especially in the capital city of the country and regional capitals, including Bahir Dar. Real estate prices have doubled and even tripled in a few years because of different factors. Thus, the purpose of this study is to analyse the factors affecting residential real estate prices in Ethiopia, particularly in Bahir Dar City. For this purpose, the study used both purposive and stratified random sampling techniques. Descriptive and inferential statistics were used to analyze the data, and the ordinary least squares method was used to identify the factors that influence residential real estate prices. The hedonic regression model result reveals that R<sup>2</sup> statistics (0.880) indicate that 88% of the variation in residential real estate prices can be explained by variation in the independent variables. Hedonic regression model results revealed that plot size, floor area, number of rooms, age of the house, external wall finishing material, ceiling finishing material, the direction of the house, distance of the property from the main road, and involvement of brokers in the transaction process, were major micro-factors that had a significant effect on residential real estate prices at the 5% level of significance. Furthermore, the interview results show that the challenges faced by real estate developers mostly include loans and aid issues, high-interest rates, mortgage restrictions, the financial capacity of real estate companies, delays in plan approvals, and rising costs of construction materials are among the major challenges affecting investment in real estate. The study suggests that the municipality of Bahir Dar City should provide an adequate supply of land to real estate developers, improve infrastructure development, and federal and regional governments should provide the credit lending mechanism by lowering interest rate especially for low-income class.

Sustainability and affordability of building electrification: A state-by-state holistic approach for multifamily buildings

This paper explores the evolving narrative of building electrification, considering its potential to solve climate change. Previous research has predominantly focused on hybrid renewable energy portfolios and increasing home pricing premiums, overlooking the perspectives of renters burdened by housing costs. Moreover, existing studies have primarily examined the effects of electrification on single-family homes and case studies, neglecting multifamily buildings and their relationship with the grid. This comprehensive study leverages calibrated building energy models to address these gaps and evaluates sustainability through carbon dioxide emissions and affordability through economic performance. The findings demonstrate significant progress in electrification since 2017, with nearly all states showing decreased energy usage in the electrified models. Environmentally, twenty-two states perform better or comparably with the electrified models in the most recent study. Economically, challenges persist, but a sensitivity analysis demonstrates how results could improve soon. Furthermore, the study discusses the outlook of electrified multifamily buildings, considering ongoing decarbonization plans for the electric grid. In three case studies, electrification demonstrates improvements in over half of the states by 2026. Overall, the research highlights the importance of expanding analysis to include multifamily buildings and emphasizes the positive impact of electrification as a viable energy, environmental, and economic solution.

Persistent trends and inefficiencies in the Greek housing market: a sentiment based approach

PurposeWe examine the aggregate price trend of the Greek housing market from a behavioral perspective.Design/methodology/approachWe construct a behavioral real estate sentiment index, based on relevant real estate search terms from Google Trends and websites, and examine its association with real estate price distributions and trends. By employing EGARCH(1,1) on the New Apartments Index data from the Bank of Greece, we capture real estate price volatility and asymmetric effects resulting from changes in the real estate search index. Enhancing robustness, macroeconomic variables are added to the mean equation. Additionally, a run test assesses the efficiency of the Greek housing market.FindingsThe results show a significant relationship between the Greek housing market and our real estate sentiment index; an increase (decrease) in search activity, indicating a growing interest in the real estate market, is strongly linked to potential increases (decreases) in real estate prices. These results remain robust across various estimation procedures and control variables. These findings underscore the influential role of real estate sentiment on the Greek housing market and highlight the importance of considering behavioral factors when analyzing and predicting trends in the housing market.Originality/valueTo investigate the behavioral effect on the Greek housing market, we construct our behavioral pattern indexes using Google search-based sentiment data from Google Trends. Additionally, we incorporate the Google Trend index as an explanatory variable in the EGARCH mean equation to evaluate the influence of online search behavior on the dynamics and prices of the Greek housing market.

The Interaction Mechanism of Real Estate Market of Typical Cities in China: Analysis Based on VAR Model

As an important part of the national economy, the real estate market is closely related to macro policies, investor confidence, etc., and at the same time shows regional heterogeneity. This study explores the real estate market in four typical cities in China (Beijing, Xi'an, Xiamen, and Chengdu) and analyzes the correlation between real estate prices, sales area, real estate development investment, market investor confidence, and money supply. Through analysis based on the VAR model, it is found that the real estate markets in different cities present their own specific interactive influence patterns. The various factors in Beijing are more closely related and have a continuous impact; other cities have similar characteristics, and the impact of each factor is somewhat different. Therefore, when formulating real estate macro-management policies, it is necessary to consider the market characteristics of different cities and the comprehensive impact of external factors to formulate more targeted strategies and mitigate the potential impact of market fluctuations on the economy.