Real Estate Prices Research Articles

Forecasting Real Estate Price via Support Vector Machine

This paper focus on the analysis of real estate price using support vector machine. The data set of a local estate price gives the details of the house including the price, date, bedroom numbers and postcode (location), and has been displayed as the line charts. There is comparison about different forecasting methods done by previous researchers including the simple moving average and ARIMA model. The data set is then fed to the model for machine learning and price prediction support vector machine. Moreover, R language is utilised to compute the support vector machine method by e1071 package and graph plotting of different kernel functions inside the support vector machine function. Then the graphs are analysed based on the accuracy of the correct estimation on the diagonal of the matrix generated. Possible improvements including the data collection and survey method are discussed for a higher precision and accuracy result for further study.

Sea level rise risks, adaptation strategies, and real estate prices in Singapore

Sea level rise risks, adaptation strategies, and real estate prices in Singapore

Market Value Appraisal of Virtual Property From A Chinese Legal Perspective

Market Value Appraisal of Virtual Property From A Chinese Legal Perspective

거시경제변수가 수도권 및 비수도권 주택가격에 미치는 요인분석

Korea's real estate policy has been preparing real estate measures only with supply and demand. It is necessary to study the changes in real estate prices of macroscopic economic variables. This is because the real estate market is complex and affected by various external factors. Unlike previous studies, this study analyzed the effects of macroeconomic variables on apartment houses in the metropolitan area and apartment houses in the non-metropolitan area, and the results of the study using the cointegration test, Granger causal variables, and impact response analysis are as follows. In the case of houses in the metropolitan and non-metropolitan areas, the amount of money and the housing jeonse price index, which are macroscopic economic variables, are commonly affected, but there is a difference in the magnitude and timing of their influence. Mortgage interest rates and loan amounts initially had a negative effect, but it was concluded that even if interest rates and loan amounts increased over time, the actual transaction price index of houses in the metropolitan area rose. The real transaction price index of houses in the non-metropolitan area was also found to have a close effect on the mortgage interest rate and average income per household.

A Study of the Quantitative Impact of Different Credit Policies on Real Estate Price Volatility in the Housing Finance Market

Real estate industry is an important industry of national economy in any country, which constitutes an important element of the wealth of the whole society and has a stable and long-term impact on national economy and social development. Based on the price theory of supply-demand equilibrium and the theory of real estate cycle fluctuation, the article combines the stock-flow model and vector autoregression model of real estate price to establish a quantitative analysis PVAR model of credit policy on real estate price fluctuation. Data from 31 provincial regions in China from 1998 to 2023 are selected as the research samples, and the specific effects of different credit policies on real estate price volatility in the housing finance market are verified through empirical analysis. When the scale of credit policy expands by 1 percentage point, real estate prices will increase by 0.00611 percentage points, while credit interest rates have a positive impact on real estate price volatility for 6 to 7 periods, and then change to a negative impact. Different regions under the credit policy on real estate price volatility there are differences, and the faster the growth rate of real estate prices in the region credit policy on real estate prices to promote the role of the more obvious. Real estate price regulation needs to accurately combine regional and economic development, so as to optimize the credit policy of the housing finance market, and then ensure the stability of real estate prices.

The Importance of Residential Real Estate Characteristics in the Assessment of Selected Groups of Real Estate Market Participants

Abstract The price and market value of real estate is influenced by a number of attributes of an economic, legal, physical-technical and environmental nature. Some market characteristics, such as location, neighborhood or area, have a universal dimension, while others result from the specifics of the analyzed real estate segment. The purpose of the survey was to assess the importance of residential real estate characteristics by real estate market participants. Two groups were surveyed. The first was made up of potential buyers and the second was made up of professionals associated with the real estate market (including appraisers, real estate agents). The results obtained were subjected to comparative and selected cross-analysis and discussion.

Research on Population Aging, Effectiveness of Monetary Policy, and Real Estate Prices

As the global trend of population aging intensifies, the multifaceted impacts of aging on the economy are becoming increasingly apparent, especially in the areas of monetary policy and the real estate market. This paper aims to explore the intrinsic relationship between population aging, the effectiveness of monetary policy, and real estate prices. By analyzing the impact of demographic changes on consumption, savings, and investment behavior, the paper further reveals how aging affects the transmission mechanism of monetary policy and examines the role of monetary policy in real estate price fluctuations. The paper begins by defining the current status of population aging and discussing its broad economic impacts, followed by an analysis of the tools and transmission mechanisms of monetary policy and its specific effects on the real estate market. Based on the construction of a theoretical model, this paper also empirically tests the responses of the real estate market to monetary policy in different countries and regions facing aging societies. Finally, based on both theoretical and empirical research results, policy recommendations are made regarding the adjustment of monetary policy and regulation of the real estate market in the context of an aging society. This study provides theoretical support and practical guidance for policymakers to address the challenges that population aging brings to economic policy.

Корреляционно-регрессионный анализ как инструмент прогнозирования прибыли организации

The planning process is important for the rhythmic functioning of an organization. Planning at each stage of the production process leads to forecasting financial results, in particular the profit from sales of the organization. As a rule, in the process of forecasting profit from sales, only internal factors or processes that influence the organization’s activities are taken into account. The purpose of this paper is to substantiate the use of correlation and regression analysis as a tool for forecasting profits from a sales organization. The paper considers two approaches to forecasting profit from sales, taking into account, among other things, the influence of external factors affecting organizations whose main activity is related to the rental and management of its own or leased real estate. The first approach to profit forecasting is based on the direct counting method and economic and mathematical algorithms. The second approach is the use of correlation and regression analysis, based on the influence of selected external factors. Profit forecasting using correlation-regression analysis showed that the average rental price of commercial real estate has a negative relationship with sales profit, and the amount of electricity consumed has a positive relationship with profit from sales. Based on the results of the correlation analysis, it can be concluded that changes in the rental price of commercial real estate and the volume of electricity consumed affect the company’s sales profit. The value of the forecast sales profit obtained under the optimistic scenario, calculated by the direct calculation method, is close to the forecast values calculated by the methods of correlation and regression analysis, which proves the importance of using this forecasting tool.

Exploring Internal Factors that Affect Real Estates Prices: Evidence from Singapore

The purpose of this paper is to analyze the internal factors that generate the difference in real estate prices in Singapore. The objectives are to achieve a deeper understanding of the reason why prices of real estate are different at the same period and to provide a standard for investors to detect real estate with prices that are underestimated and consider whether to invest or not. This paper examines intrinsic factors that mainly affect real estate prices. Regression analysis is the core methodology to analyze the factors; the author used normalization to process the data. The result shows there is a strong relationship between the price of real estate and house age, distance to the nearest MRT station, the number of convenience stores, and the latitude. It can be discovered that the price of real estate negatively correlates to the house age and the distance to the nearest MRT station; positively corresponds to the number of convenience stores and the latitude.

Research on the Determinants of Real Estate Prices in China under Supply and Demand

Since the reform and opening up, the real estate industry has gradually become the pillar industry of the Chinese people, directly or indirectly promoting the development of macro-economy. However, in recent years, the continuous rise of housing prices has become the focus of attention of the government and all sectors of society, which has triggered extensive and in-depth discussion and attention on the real estate industry. Real estate prices are not only closely related to people's lives, but also related to the development of cities and the long-term stability of the country. The healthy development of the real estate industry is also related to the composition of household wealth and government revenue, as well as the stable development of the national economy. Therefore, it is of great practical significance to pay attention to the changes of housing prices. This paper mainly summarizes and reviews the relevant literature on the analysis of factors affecting house prices at home and abroad from three aspects: the supply side, the demand side and the hedonic price model of real estate. It provides basic underlying logic for academic research, policy-making, practical application and individual house purchase decision-making.

Research on the Influencing Factors of House Prices in Sydney

Considering various factors that affect house prices, this study investigates whether housing prices in Sydney can maintain their value or even increase in the future. A multiple linear regression model was applied using a dataset obtained from Kaggle, which includes 11,161 real estate transactions in Sydney from 2016 to 2021. This paper uses it to explore the relationship between housing prices and 12 independent variables (originally 14 but some have to be abandoned), such as the number of bedrooms, property size, median suburban income, and distance to the central business district (CBD). The results indicate that some factors have a positive, negative, or no impact on house prices and that 39.6% of the real estate price is influenced by them, indicating a moderate level of predictability. These findings provide valuable insights for investors and policymakers interested in the dynamics of the Sydney real estate market, emphasizing the importance of geographic location and property attributes in determining housing prices.

Application of robust estimation methods in real estate valuation

Motives: Conducting real estate appraisals in well-developed markets presents a multitude of data analysis challenges. Some property price data may contain outliers that can significantly affect the valuation process and, as a result, the estimated value. In the case of real estate valuation regression models, estimation is most often based on the least squares method, where outliers are taken into account just like the rest of the data. Eliminating or minimizing the influence of outliers can lead to more reliable estimation results. Such problems can be solved by implementing robust regression methods. Aim: The main goal is to determine whether robust estimation methods using M-estimators with multiple regression models can provide more accurate estimates of property values. Results: Research has shown that multiple regression models using robust regression methods can be applied to estimate property values. The use of different types of M-estimators allows for the objective elimination of outliers through algorithms that operate on the entire data set. The calculations are carried out iteratively, and at each iteration step the residuals are verified and the observations are re-weighted. The following M-estimators were considered: Huber, Hampel, Tukey, Faire, Cauchy and Welsch. The reference point was the estimation results from the ordinary least squares method (OLS). All analysed M-estimators led to an increase in the coefficient of determination value and a decrease in standard estimation errors. Each algorithm detected the outliers. The valuation results for the selected properties were also more reliable. The results obtained depend on the characteristics of the data, and the choice of the best estimator may vary across different property markets. The selection of the best estimator may even vary within the same local property market, where the valuer makes subjective assessments of the location or other attributes.

Co-movement of real estate prices: a study of Brazil’s three largest cities

The objective of the paper is to verify the existence of transmission of residential real estate prices among Brazilian cities. The strategy adopted was to analyze the time series of prices and estimate an Autoregressive Vector Model - VAR for panel data and Granger causality tests for the three largest cities in Brazil (São Paulo, Rio de Janeiro and Belo Horizonte) between the period 2009-2017. The results point out that each local market has the determining power in price formation. However, an important result of the study was to verify transmission relationship between cities.

Linear and Nonlinear Modelling of the Usable Area of Buildings with Multi-Pitched Roofs

One of the key elements in real estate appraisal of residential buildings is the usable area. To determine the monetary value of real estate, appraisers in Poland often rely on transaction data registered in the Real Estate Price Register (REPR). However, the REPR may contain meaningful gaps, particularly on information concerning usable areas. This may lead to difficulties in finding suitable comparative properties, resulting in mispricing of the property. To address this problem, we used linear and nonlinear models to estimate the usable area of buildings with multi-pitched roofs. Utilizing widely available data from the Topographic Objects Database (BDOT10k) based on LiDAR technology, we have shown that three parameters (building’s covered area, building’s height, and optionally the number of storeys) are sufficient for a reliable estimate of the usable area of a building. The best linear model, using design data from architectural offices, achieved a fit of 95%, while the best model based on real data of existing buildings in the city of Koszalin, Poland achieved 92% fit. The best nonlinear model achieved slightly better results than the linear model in the case of design data (better fit by approximately 0.2%). In the case of existing buildings in Koszalin, the best fit was at 93%. The proposed method may help property appraisers determine a more accurate estimation of the usable area of comparative buildings in the absence of this information in the REPR.

AI-driven valuation: a new era for real estate appraisal

PurposeThis paper explores the emergence of artificial intelligence (AI) in real estate valuation, analysing its potential to enhance accuracy, efficiency and transparency in the appraisal process and examines the implications of this technological shift for various stakeholders.Design/methodology/approachThis research adopts a comprehensive literature review approach, drawing upon existing research in real estate, computer science and related fields. A systematic analysis of scholarly publications and industry trends was conducted to examine the underlying technologies, diverse applications, potential benefits, inherent limitations and future trends associated with AI-driven valuation models, including machine learning and deep learning methods.FindingsThe study reveals that AI-powered valuation models offer significant advantages over traditional appraisal methods, including enhanced accuracy, increased efficiency, reduced costs and improved risk management. However, critical challenges related to data bias, algorithmic transparency (the “black box” problem), and the need for human oversight must be addressed to ensure responsible and effective AI implementation.Originality/valueThis paper provides insights into the transformative potential of AI in real estate valuation for a wide range of stakeholders. Real estate professionals can gain a better understanding of how AI can enhance their decision-making processes, improve efficiency and mitigate risk. Appraisers will benefit from the paper’s analysis of evolving skillsets required in the AI era, while lenders and investors will gain a clearer understanding of how AI-driven valuation can strengthen risk assessment and portfolio management. Policymakers can utilize this research to inform the development of ethical guidelines and regulations for AI adoption in real estate appraisal.

The Citizens´ Psychological Perception of Housing in the Environment of the Housing Crisis in Central Europe. Pardubice Case Study, Czechia

One of the problems in contemporary Europe are the high real estate prices, which do not allow young couples to start their professional careers and family life in places that would suit them. Such problem causes social and economic problems in society. Public administration is trying to create building-favorable conditions at the level of the state, regions, or counties, as well as individual municipalities. Market pressures on rising housing prices have at least partially eased. In order for such a procedure to achieve the desired results, it must be a coordinated policy developed and implemented in accordance with modern approaches to the management of municipalities, cities and regions. Such a policy also includes the principles of strategic planning, in the case of housing issues summarized in the concept of housing development. The aim of the paper is to identify the key points of the process of preparing the housing concept of a medium-sized city.

Global Transmission Shocks of U.S. Monetary Policy on China's Housing Prices

The paper demonstrates that the monetary policy of the United States has important spillover effects to China. Based on the monthly data from January 2016 to April 2024, this paper establishes a VAR model to analyze the influence of U.S. monetary policy on China's real estate prices and its transmission channels, including interest rate, exchange rate, consumer confidence, and asset price. The results show that the impact of the Federal Funds Effective Rate on housing prices is very quick, reaching a maximum in the first month and continuing to have a positive impact over the long term. The Federal Funds Effective Rate and China's Loan Prime Rate have a positive impact on housing prices in the early stage, but then there is a change in impact direction. The Exchange Rate, Consumer Confidence Index and CSI 300 index all had positive spillover effects on housing prices in the early days. The variance decomposition analysis shows that the exchange rate channel plays the most significant role in causing China’s real estate price fluctuations.

Application of the hedonic pricing model to real estate valuation in Hanoi, Vietnam

The value of real estate is determined by the relationship between supply and demand in the market, location, and the physical structure of the land and surrounding area. During the development of infrastructure in the capital area of Hanoi, Vietnam, changes have occurred that directly impact housing prices. This study applies the Hedonic pricing model and asking prices to predict the market value of real estate and establish the correlation between the factors affecting land prices in Hanoi in 2023 and 2024. The results show that six main factors influence real estate prices: area, number of bedrooms, proximity to the city center, type, amenities, and structure. Among these, proximity to the city center has a negative impact, while the remaining factors have a positive impact on housing prices in this area. Based on these results, participants in the real estate market can make accurate predictions about housing prices, and market regulators can gain a comprehensive view of this market, particularly the factors influencing housing prices.

Forecasts of residential real estate price indices for ten major Chinese cities through Gaussian process regressions

Forecasts of residential real estate price indices for ten major Chinese cities through Gaussian process regressions

Algorithm-Driven Hedonic Real Estate Pricing – An Explainable AI Approach

AbstractData-driven machine learning algorithms triggered a fundamental change in hedonic real estate pricing. However, their adaptive nonparametric structure makes inference and out-ofsample prediction challenging. This study introduces an explainable approach to interpreting machine learning predictions, which has not been done before in the local market context. Specifically, Random Forest and Extreme Gradient Boosting models are developed for residential real estate price prediction in Warsaw in 2021 on 10,827 property transactions. Model-agnostic Explainable Artificial Intelligence (XAI) methods are then used to investigate the black box decision making. The results show the practicability of applying XAI frameworks in the real estate market context to decode the rationale behind data-driven algorithms. Information about the relationships between input variables is extracted in greater detail. Accurate, reliable and transparent real estate valuation support tools can offer substantial advantages to participants in the real estate market, including banks, insurers, pension and sovereign wealth funds, as well public authorities and private individuals.