Real Estate Prices Research Articles

Crisis of housing cooperatives in Mexico City: A struggle for Palo Alto

This article features a conversation with Luis Márquez Cruz and Fabiola Carrera from the Palo Alto Cooperative, delving into the history and ongoing struggles of one of Mexico City's oldest housing cooperatives. Formed in the 1980s based on principles of solidarity, mutual aid, and self-management, the cooperative provided affordable housing to working-class families. However, changes to cooperative laws in 1994 halted new cooperative projects and shifted government support to private developers. Today, Palo Alto faces internal divisions as some members push for the cooperative's dissolution and sale of property amidst rising real estate prices, while others fight to preserve collective ownership. The article explores the cooperative’s international alliances, political training, and grassroots organizing efforts, highlighting the urgency of defending cooperative housing as an alternative to Mexico City's increasingly commodified and exclusionary housing market.

The Current Situation of the Housing Market Bubble in Chinas First-tier Cities

Abstract: With the progression of China's economy and urbanization, the real estate industry experienced rapid growth, accompanied by increasing speculative activities, which led to a continuous rise in housing prices and the formation of a bubble. Therefore, the focus of this study was the current state of China's real estate bubble and potential solutions. Given the issue of excessively high real estate prices, this study specifically examined the price trends and factors contributing to the real estate bubble in four Chinese cities: Beijing, Shanghai, Guangzhou, and Shenzhen. The research methodology was as follows: First, data on the price-to-income ratio was collected to analyze the current state of housing prices in China, revealing that housing prices were still far above their actual value and beyond a reasonable range, thereby indicating the severity of the real estate bubble in China. Second, by analyzing data and reviewing the literature, the factors contributing to China's real estate bubble were identified and categorized into two main groups: policy-related and economic. On the policy side, factors included policy reforms, monetary easing, and reductions in both the benchmark interest rate and the reserve requirement ratio. On the economic side, factors included the growth in per capita disposable income and speculative behavior. Finally, this paper analyzed how a property tax could curb speculative activities by increasing the cost of purchasing properties, thereby reducing demand and speculative behavior. The findings revealed that the real estate bubble in China's first-tier cities remained severe, and that a property tax could help curb speculative activities and slow the pace of bubble expansion.

Forecasting residential real estate prices and energy usage in Flanders

Purpose Currently, residential forecasting literature is limited with few to no forecasting papers that consider residential prices and energy consumption for the selling and rental market within a single study. The purpose of this paper is to study the prices and energy usage for both markets by combining an explanatory model with the predictions of a predictive model, which will lead to a better understanding of the forecasting results. Design/methodology/approach This study presents an econometric comparison between 508,612 selling properties and 174,007 rental properties transacted in Flanders between January 2019 and December 2023. The authors study their prices and energy usage by combining the causal results of a hedonic price model (HPM) with the predictions of an autoregressive integrated moving average model (ARIMA). Findings The authors find that both markets are extremely sensitive to changes in related factors, whereby selling prices are primarily influenced by macroeconomic factors, but rental prices and especially energy usage are also sensitive to market standards. Policies that target energy efficiency can therefore steer its future trend. In addition, in line with previous literature, the HPM shows an increasingly strong relationship between residential prices and energy efficiency over the years, indicating that these steering energy policies also indirectly monitor residential prices. Originality/value This study underscores the importance of integrating the results of an explanatory model with a predictive model. It demonstrates that combining them provides a more comprehensive understanding which is helpful for policymakers, real estate professionals and investors during their decision-making process.

Would carbon tax policy promote real estate prices?

Real estate plays a pivotal role in carbon emissions. Carbon tax policy is a crucial measure for mitigating enterprises' carbon emissions. However, imposing a carbon tax on productive enterprises in the real estate industry to reduce emissions may increase their production costs, which could easily be passed on to buyers, thus inflating real estate prices and impeding policy effectiveness. To examine the impact and negative consequences of carbon tax policy applied to the real estate industry, we develop a dynamic stochastic general equilibrium model encompassing building materials manufacturers, real estate firms, and the environmental regulatory government. Findings indicate that implementing the carbon tax policy significantly reduces carbon emissions in the real estate industry. However, the existence of a price transmission mechanism makes carbon tax policies susceptible to driving up real estate prices. Further welfare analysis shows that increasing carbon tax rates will result in social welfare losses, allowing the government to modify and refine its policy objectives.

Machine Learning Valuation in Dual Market Dynamics: A Case Study of the Formal and Informal Real Estate Market in Dar es Salaam

The housing market in Dar es Salaam, Tanzania, is expanding and with it a need for increased market transparency to guide investors and other stakeholders. The objective of this paper is to evaluate machine learning (ML) methods to appraise real estate in formal and informal housing markets in this nascent market sector. Various advanced ML models are applied with the aim of improving property value estimates in a market with limited access to information. The dataset used included detailed property characteristics and transaction data from both market types. Regression, decision trees, neural networks, and ensemble methods were employed to refine property appraisals across these settings. The findings indicate significant differences between formal and informal market valuations, demonstrating ML’s effectiveness in handling limited data and complex market dynamics. These results emphasise the potential of ML techniques in emerging markets where traditional valuation methods often fail due to the scarcity of transaction data.

Catastrophic fires, human displacement, and real estate prices in California

Millions of people are displaced by natural disasters each year, yet little is known about how evacuees affect host communities. We analyze the migratory effects of the most destructive fire in California history, the 2018 Camp Fire, which destroyed over 18,000 structures and displaced roughly 50,000 people. By merging geospatial information on the fire’s footprint with Zillow’s housing transaction data, we estimate both the spatial and temporal effects of the fire on real estate prices at a granular level. A number of important insights emerge. First, within 25 miles of the fire’s footprint, home prices increased by 13 percent in the six-week aftermath of the fire. Effects decay with distance and are statistically insignificant beyond 100 miles. Second, effects are detected within two weeks of the fire, fully materialize within four weeks, and are persistent up to ten months (which exhausts our period of consideration). Results are consistent with the relocation decisions of evacuees and are robust to a variety of specifications and modeling assumptions.

The influence of urban green and recreational areas on the price of housing in Zagreb

Urban green and recreational spaces are important for residents and one of the foundations for the functioningof cities. They offer numerous benefits, including the influence on the price of real estate as an economic benefit.In this paper, the influence of green and recreational areas in Zagreb on the price of apartments in residential buildings is analysed using the hedonic pricing model. The data for the study was processed and analysed using a geographic information system. A local hedonic price model was developed. The results show that some of the observed green and recreational areas increase the price of housing due to their proximity, while others decrease it. Some areas have no influence on the price of housing. The influence of the observed areas on housing prices was heterogeneous. Spatial differences were also found in the influence of the observed areas on housing prices. The most attractive factors when choosing a place to live are Jarun and the parks in the city centre. The price of apartments that near the Jarun increases by 41 EUR/m2 if the apartment is 100 m closer to the Jarun. The price of the apartment increases by 91 EUR/m2 the closer the apartment is to the parks in the city centre (per 100 m). Apartments near Jarun and the parks in the city centre are not available for residents with lower socio-economic status. The forest areas, Maksimir Park and the banks of the Sava river are not favoured when choosing a place to live. Neighbourhoods near these areas are available to the lower socioeconomic status population. Bundek raises the price of one part of the apartments and lowers the price of the other. The results of this study can contribute to the study of green gentrification in Zagreb, but further research is needed on socio-economic indicators and other changes in the region.

Housing Price Prediction - Machine Learning and Geostatistical Methods

Abstract Machine learning algorithms are increasingly often used to predict real estate prices because they generate more accurate results than conventional statistical or geostatistical methods. This study proposes a methodology for incorporating information about the spatial distribution of residuals, estimated by kriging, into selected machine learning algorithms. The analysis was based on apartment prices quoted in the Polish capital of Warsaw. The study demonstrated that machine learning combined with geostatistical methods significantly improves the accuracy of housing price predictions. Local factors that influence housing prices can be directly incorporated into the model with the use of dedicated maps.

Analysis of the Combined Impact of Socio-Economic Factors on House Price Volatility in the Housing Market

As an important variable in China’s economic system, real estate price is a key point closely linked to the country’s economy and people’s livelihood. It is of great practical significance to study the impact of socio-economic factors in the housing market on China’s house prices and its transmission mechanism, so as to put forward corresponding policy recommendations for stabilizing house prices and preventing real estate market and financial risks. Taking China’s time series data from 2014 to 2023 as a sample, this paper empirically investigates the law of the relationship between socio-economic factors in the housing market and house price fluctuations by constructing a structural vector autoregressive model, and employs impulse response function and variance decomposition to analyze the relationship between the two dynamically. The study shows that the 15-period variance decomposition effect of itself and residents’ disposable income are the main influencing factors of house price fluctuation, and the degree of explanation of its fluctuation is 55.5% and 23.2% respectively. In a market economy, real estate prices fluctuate up and down around their value due to the law of value. Therefore, real estate prices cannot rise indefinitely, and the final result should be stabilized within a reasonable level.

A wall between real estate valuation standards and professional appraisal practice: A focus on Italian market

Real estate appraisal standards provide guidelines for the preparation of reliable valuations. These standards emphasize the central role of market data collection in market-oriented valuation methodologies such as the Market Comparison Approach (MCA), which is the most commonly used. The objective of this study is to highlight the difficulties in data finding, as well as the gap between the standards and the actual appraisal practices in Italy. Thus, a detailed comparison was made between the real estate data considered necessary by the standards and those ones reasonably detectable by appraisers, showing that some important market information is not reachable due to legal, technical and economic factors. Finally, a case study is presented in which the actual appraisal of a residential property is schematically described to support what is claimed with the research question and thus the degree of uncertainty around an estimate judgment.

On the uncertainty of real estate price predictions

ABSTRACT Uncertainty quantification associated with real estate appraisal has largely been overlooked in the literature. In this paper, we address this gap by analysing the uncertainty in automated property valuations using conformal prediction, a distribution-free procedure for constructing prediction intervals with valid coverage in finite samples. Through an empirical study of property prices in the San Francisco Bay Area, we find that prediction intervals obtained using conformal quantile regression have exact coverage. In contrast, prediction intervals obtained from non-conformal quantile regressions severely undercover the data. Furthermore, we show that the intervals adapt to various characteristics of the dwellings, which is crucial given the heterogeneous nature of real estate data. Indeed, we observe that larger and older properties, those in both low and high-income neighbourhoods, as well as those on the market for less than one year are more challenging to evaluate.

The Impact of the Opportunity Zone Program on Residential Real Estate

Problem definition: Opportunity zones (OZs) are designated census tracts in which real estate investments can gain tax benefits. Introduced by the U.S. Tax Cuts and Jobs Act of 2017, the goal of the OZ program is to foster economic development in distressed neighborhoods. In this paper, we investigate and optimize the OZ selection process and examine the impact of OZs by exploiting two data sets: a proprietary real estate data set that includes 36.1 million residential transactions spanning all 50 U.S. states and census-tract demographics data between 2010 and 2019. Methodology/results: We show that census tracts with higher poverty and unemployment rates were more likely to be selected. Counterintuitively, however, tracts with a higher average real estate price were also more likely to be selected. We then apply difference-in-differences, synthetic control, and matching techniques to rigorously assess the impact of the OZ program on two key real estate metrics: price and transaction volume. We find that the OZ program increased real estate prices by 4.03%–6.13% but do not observe a significant effect on the transaction volume. We also find that investors primarily targeted the high-end real estate market, namely, exhibiting a cherry-picking behavior. To better fulfill its intended societal and economic goals, we propose an optimization framework with fairness considerations for OZ assignment decisions. We show that the OZs assigned from our fairness-aware optimization formulation can better serve distressed communities and mitigate investors’ cherry-picking behavior. Managerial implications: Our paper underscores the importance of incorporating fairness in OZ designation to achieve a desirable real estate market reaction. Our large-scale empirical analysis provides a comprehensive assessment of the current government OZ assignment, and our fairness-aware optimization framework provides concrete recommendations for policy makers. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.0746 .

STATE AND PROSPECTS OF THE DEVELOPMENT OF UKRAINE'S BUILDING INDUSTRY IN THE CONDITIONS OF THE ECONOMIC CRISIS CAUSED BY THE FULL-SCALE AGGRESSION OF THE RUSSIA, FROM THE POINT OF VIEW OF ECONOMIC CYCLICITY

The article is dedicated to examining the specific manifestations of economic cyclicality in Ukraine's building materials industry under martial law conditions. The analysis reveals that the full-scale war has led to the destruction of approximately 20% of enterprises, while others have experienced significant declines in production volumes, market access, raw material sources, and human resources. The features of war-induced economic crises are compared with cyclical overproduction crises, showing that military actions immediately push the economy into a recession phase. However, recovery may commence even before the end of hostilities due to the economy reaching the trough of the economic cycle.The article explores various approaches to defining and understanding the concept of "economic crisis," highlighting the importance of identifying its causes and characteristics. Key factors influencing the transition between different phases of economic cycles—recession, depression, recovery, and expansion—are identified. It is noted that the moments of phase transition are critical to economic development, as they represent the shift from quantitative changes in one phase to qualitative changes in the next.The dynamics of real estate prices are analyzed as one of the key indicators signaling the onset of the recovery phase. Additionally, the article formulates the components of a development strategy for Ukraine’s construction sector during the recovery period, taking into account the consequences of the war. These components include the localization of building materials production, improving housing comfort, ensuring the environmental sustainability of building materials, conserving resources in their production (particularly energy), involving foreign aid in the reconstruction process, and recycling demolition waste into building materials. It is determined that the future development of the construction industry must also address the issue of effectively insulating protective structures and bomb shelters with eco-friendly materials, necessitating further development of domestic building materials that meet the urgent demands of wartime. The article concludes with the prospect of utilizing hemp-lime building materials in the reconstruction process, as they fully align with the proposed development strategy

Comparative Analysis Between Japan's Real Estate Economy and China's Real Estate Status

Japan's real estate bubble economy in the 1990s caused a major blow to the Japanese economy, Japan entered the "disappearing 30 years". The causes of Japan's real estate bubble economy and the reasons why the bubble burst are worthy of the world's countries to learn from. 21st century since the rapid development of China's real estate economy, housing prices are growing rapidly. The real estate economy has been overheated in some areas, although the Chinese government has taken relevant measures to cool down, still can't relax the vigilance, people should always pay attention to the overheated areas of real estate prices, land prices. Through the comparison between China and Japan, although the economic structure and development trend are similar when the property overheating phenomenon occurs, the economic development background and international background between the two countries are different, and the governments of the two countries have adopted different strategies to deal with the property overheating phenomenon. So in conclusion, although there are some areas in China where real estate is overheating, China's real estate market will not burst the bubble. In order to prevent China's real estate bubble from bursting and its impact on economic development, China should pay attention to the reasons behind it and make targeted policy adjustments, while also drawing on the lessons learned from other countries to promote the healthy and stable development of the real estate economy.

Identifying the key variables affecting condominium real estate appraisal in the context of Al Bireh City, Palestine: a literature and questionnaire-based approach

Purpose Due to the lack of consensus on influential variables for real estate appraisal, which varies from one country to another based on the national preferences and customs of each country, this study aims to identify the most influential variables affecting condominium apartment real estate appraisal within the context of Al Bireh city, Palestine. Design/methodology/approach The methodology adopts a cross-sectional quantitative approach, entailing the administration of an online questionnaire survey to 103 buyers and appraisers. The questionnaire aims to evaluate 32 variables concerning their impact on condominium real estate appraisal. Out of these, 25 are derived from three specific previous studies, and the remaining 7 are identified through various studies or by the authors, taking into account the local context and the geopolitical situation in Palestine. Respondents assign scores to these variables on a five-point Likert scale, ranging from 1 to 5, where 1 indicates less influence and 5 signifies the most influence. Variables with an arithmetic mean score exceeding 4 are deemed the most influential. Findings The findings underscore 16 and 17 influential variables as perceived by buyers and appraisers, respectively. Notably, 13 variables are common, including aspects such as parking, elevator, neighborhood, floor apartments, finishing quality, construction material, condition, building apartments, area, open sides, building floors, colonies and age. Research limitations/implications The primary constraint of this study is its dependence on insights solely from buyers and appraisers, disregarding input from other stakeholders like investors or developers. The questionnaire lacks vital respondent characteristics, such as gender and occupation, impeding the analysis of variable dependence on participant attributes. Although some additional influential variables are suggested through the responses of an open-ended question, the questionnaire is not repeated, leaving their influence unassessed. This study's focus on Al Bireh city limits the opportunity for result comparison with other cities, diminishing its generalizability. Practical implications The implications of this research are twofold: to provide stakeholders with a checklist for variables influencing apartment price value and to guide data collection related to the real estate appraisal sector, facilitating its use as input in advanced appraisal methods such as artificial intelligence with a view to improving overall performance. Obtaining an informed, mature and accurate appraisal has direct economic, business and financial impacts at the level of policymakers and individuals. Originality/value To the best of the authors' knowledge, this study is the inaugural endeavor in Palestine focusing on identifying pivotal factors influencing condominium apartment appraisal values. This study concludes by presenting a checklist comprising the most influential variables, offering utility to various stakeholders, including buyers, appraisers and developers. In addition, the questionnaire incorporates an open-ended question, soliciting respondents' input on additional variables they believe impact the appraisal process.

Impact of the Fed's Rate Hike Policy on the Prices of Commodities and Real Estate

The United States has experienced a long period of stubborn inflation since 2022, so the Federal Reserve began to raise interest rates in March 2022, which will cause asset price volatility and valuation changes. The purpose of this study is to explore the changes of some commodity and real estate prices under this background, and to put forward reasonable valuation forecast suggestions. This research mainly analyzes the price changes of crude oil, corn, and real estate under the background of the Fed's interest rate hike and analyzes the changes of asset prices by constructing the structure vector autoregressive (SVAR) model and the grid connectivity model. According to the analysis, the results show that the Fed can influence real asset prices through its monetary policy, and other factors inevitably interfere. The empirical data from March 2022 to March 2024 also show that with the Fed's multiple interest rate hikes, asset prices are affected by different degrees of decline.

Appraisal of real estate

Appraisal of real estate

The Research of Real Estate Price Bubble in Case of Hengda Real Estate Company

Real state bubble refers to a continuous process of abrupt increase of selling price which excesses value. Therefore the economy is so full of bubbles that cannot reflect material wealth. When the price is too high to afford by consumers, the whole industry have risk to collapse. In order to research reasons lead to real estate bubble, this paper uses liner regression method to explore factors that influence real state selling price. As a result, the real state selling price is positive relative to land price and resident disposable income and negatively relative to interest rate. Additionally, this research also takes a example of Hengda to prove the result.

The heterogeneous response of real estate prices during the Covid-19 pandemic

Abstract We estimate the transmission of the pandemic shock in 2020 to the residential and commercial real estate market by causal machine learning, using granular data for Germany. We exploit differences in the incidence of Covid infections and short-time work at the municipal level for the identification of epidemiological and economic effects of the pandemic. We find that (i) a larger incidence of Covid infections temporarily reduced rents for retail real estate; (ii) a larger incidence of short-time work temporarily reduced rents of office real estate; (iii) the pandemic increased prices, particularly in the top price segment of commercial real estate.

Research on Influencing Factors of China Real Estate Price Based on Multiple Linear Regression

Research on Influencing Factors of China Real Estate Price Based on Multiple Linear Regression