Real Estate Prices Research Articles

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.

Evaluating the effects of supervised consumption sites on housing prices in Montreal, Canada using interrupted time series and hedonic price models

BackgroundIn 2017, three brick and mortar supervised consumption sites (SCS) opened in Montreal, Canada. Opponents argued the sites would attract people who use drugs and reduce local real estate prices. MethodsWe used interrupted time series and hedonic price models to evaluate the effects of Montreal’s SCS on local real estate prices. We linked the Quebec Professional Association of Real Estate Brokers’ housing sales data provided by Centris Inc. with census tract data and gentrification scores. Homes sold within 200m of the SCS locations between 1 January 2014 and 31 December 2021 were included. We adjusted for internal (e.g., number of bed/bathrooms, unit size) and external attributes (e.g., neighbourhood demographics), and included a spatio-temporal lag to account for correlation between sales. For sensitivity analysis we used site-specific dummy variables to better account for unmeasured neighbourhood differences, and repeated analyses using 500m and 1000m radii. ResultsWe observed a price shock after the opening of the first two SCS in June 2017 (level effect: −10.5%, 95% CI: −19.1%, −1.1%) but prices rose faster month-to-month (trend effect: 1.1%, 95% CI: 0.7%, 1.6%) after implementation. Following the implementation of the third site in November 2017 there was no immediate impact (level effect: 2.4%, 95% CI: −10.4%, 17.0%) but once more prices roses faster (0.9%, 95% CI: 0.4%, 1.5%) thereafter. When we replaced neighbourhood attributes with a site-specific dummy variable, we observed the same pattern. Sales’ prices dropped (level effect: −9.6%, 95% CI: −15.0%, −3.8%) but rose faster month-to-month (trend effect: 0.9%, 95% CI: 0.6%, 1.2%) following June 2017’s SCS implementations, with no level effect (4.9%, 95% CI: −7.3%, 18.6%) and a positive trend (0.9%, 95% CI: 0.5%, 1.3%) after November 2017’s SCS opening. In most 500m and 1000m radii models, there were no immediate shocks following SCS opening, however, positive trend effects persisted in all models. ConclusionOur models suggest homes sold near SCS may experience a price shock immediately post-implementation, with evidence of market recovery in the months that follow.

Aging and real estate prices in Germany

AbstractExploiting regional heterogeneity in population dynamics across more than 10,000 municipalities in Germany, we provide robust empirical evidence that population aging depresses real estate prices and rents. Using millions of individual real estate listings and detailed demographic data at the municipality level, we estimate that average sales prices in 2020 would have been up to 12% higher if the population age distribution had been the same as in 2008. We show that population aging not only reduces prices but also increases the availability of real estate. In addition, we document substantial heterogeneity in price responses across dwelling type, property characteristics, and urban-rural status, suggesting that lower housing demand and life-cycle dissaving are driving our results. We predict that population aging will continue to exert downward pressure on house prices and exacerbate regional disparities in Germany.

Assessing proxies for market prices of thinly traded assets with scheduled cash flows

Pseudo-market prices of infrequently traded assets with scheduled cash flows – commercial real estate appraisals and matrix prices of commercial mortgage backed securities – are compared against a VAR model to assess the extent to which these widely-used proxies are grounded in economic fundamentals. Property appraisals fail to fully incorporate the economic fundamentals underlying commercial real estate transactions. During the financial crisis, CMBS matrix prices captured underlying economic fundamentals and exhibited little pricing bias. However, matrix prices no longer exhibited such economic discipline after the financial crisis. Incorporating VAR forecasts considerably improves the predictive ability of appraisals and matrix prices.

Beyond the collateral channel: Real estate prices and manufacturing firm investment in China

Recent studies show that real estate price appreciation raises firms’ debt capacity and therefore investment. However, appreciation of real estate prices may also push up production costs and thus discourage investment incentives. Using a large panel data set of Chinese manufacturing firms, we document that local real estate price appreciation had significant and robust negative effects on firm investment. The size of the negative effect is economically large, with a one standard deviation change in real estate prices explaining 17% of the variation in firm investment. Further exploration reveals that such negative effects of real estate prices on investment are stronger for firms in labor intensive sectors.

Real Estate Price Prediction using ANN

This report explores the use of machine learning to predict real estate prices, addressing challenges such as market dynamics and data complexity. Through extensive data analysis and feature engineering, the study implements decision trees, random forests, and neural networks to capture the intricate factors affecting property values. The performance of these models is rigorously evaluated, providing stakeholders with reliable forecasts and insights into key price determinants. This research enhances decision-making in the real estate sector by improving the accuracy and transparency of price predictions. A Graphical User Interface is also developed to enter required information and display the predicted price. Keywords—Machine Learning, Price Prediction, Artificial Neural Network

THE DIFFERENTIAL EFFECTS OF CREDIT POLICY ON REAL ESTATE PRICES FROM THE PERSPECTIVE OF CLUB CONVERGENCE

This study investigates the differential effects of credit policy in the context of real estate price convergence in China. First, we analyze the real estate price convergence of 35 large and medium-sized cities in China using [Formula: see text] convergence and club convergence methods. Subsequently, based on the convergence situation, we use panel models to compare the heterogeneous effects of the credit policy regulation of different clubs. The findings are as follows. (1) In terms of overall convergence, the findings confirm that there is no [Formula: see text] convergence in China’s real estate prices. Prices in each city do not show a trend of convergence to the total mean. (2) There are currently four convergence clubs and one divergence group in terms of regional convergence. Cities with similar initial price levels have similar structural characteristics and tend to converge to their respective local steady states. GDP per capita and population density are the main driving forces for the formation of a real estate price convergence club. (3) We show that the credit scale has a statistically positive impact on the real estate price, and the credit scales of various convergence clubs have different effects on real estate prices. Clubs with more developed real estate and financial markets are more sensitive to changes in credit policy.

Editorial

Dear Readers,  It gives me great pleasure to announce the fourth regular issue of 2024. I would like to thank all the authors for their sound research papers and the editorial board and our guest reviewers for their extremely valuable reviews and suggestions for improvement. These contributions and the generous support of the consortium members enable us to run our journal and maintain its quality. I would also like to thank our broader community for reading and incorporating sound J.UCS papers into their research. I am also very proud to announce that our journal has been included in the Free Journal Network as one of the listed high-quality journals in this field. Still, I would like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in receiving high-quality proposals for special issues on new topics and emerging trends. In this regular issue, I am very pleased to introduce six papers by 22 authors from nine countries: Brazil, Czech Republic, Germany, Iran, Jordan, Portugal, Thailand, Turkey, and the USA. In a collaboration between researchers from the Czech Republic and Germany, Sivaramasamy Elayaraja, Sunil Yeruva Vlastimil Stejskal, and Satish Nandipati look into multi-class microscopic image analysis of protozoan parasites using convolutional neural network and perform a study on 4740 microscopic images. André Diegues Fernandes, João Cruz, Miguel Mira da Silva, and Rúben Pereira from Portugal cover a systematic literature review for mapping and integrating security and risk standards. In a collaborative research between Jordan and the USA, Sahar Idwan, Junaid Zubairi, Syed Ali Haider, and Wael Etaiwi introduce their reactive traffic congestion control method based on a hierarchical graph approach. Shahrzad Riahi, Ramtin Khosravi, and Fatemeh Ghassemi from Iran conduct a study on knowledge-related policy analysis in an inference-enabled actor model. Josival Silva, Nelson Rosa, and Fernando Aires from Brazil research and present UP-Home, a self-adaptive solution that manages the security of smart homes by industry standards, and identifies and mitigates smart home security vulnerabilities. Last but not least, Sait Can Yucebas, Sukran Yalpir, Levent Genc, and Melike Dogan from Turkey discuss their research on price prediction and determination of the factors influencing real estate prices using X-Means clustering and CART decision trees. Enjoy Reading! Cordially, Christian Gütl, Managing Editor Graz University of Technology, Graz, Austria

Analysis of the initial steps of the Market Comparison Approach (MCA) for its application to agricultural land: parameters of the market segment and real estate data

In the current Italian real estate market, estimates of the market value of rural properties are carried out by appraisers mainly using the single-parameter comparison procedure and, less frequently, the income capitalization method. To overcome problems of appraiser subjectivity and other issues related to these methods, and to gradually comply with real estate appraisal standards, this research paper aims to develop and validate a scientifically rigorous method. This article carries out a first attempt to apply the mixed approach based on the integration of Market Comparison Approach (MCA) and General Assessment System (GAS) to agricultural land, currently used only in the field of urban real estate appraisal. The study focuses on the first steps of this valuation procedure: choice of the parameters which characterise the market segment and identification of the land characteristics to be included in the procedure. These two steps are preparatory to the following phase concerning the estimate of marginal prices, which represents the core of the whole valuation procedure. In the current Italian real estate market, estimates of the market value of rural properties are carried out by appraisers mainly using the single-parameter comparison procedure and, less frequently, the income capitalization method. To overcome problems of appraiser subjectivity and other issues related to these methods, and to comply with real estate appraisal standards, this research paper aims to develop and validate a scientifically rigorous method. The Market Comparison Approach (MCA) is used as a starting point for a comparative multiparameter approach, which is currently only used in the field of urban real estate appraisal.This paper carries out a first attempt to apply the MCA to agricultural land, contributing an initial exploration of developing the proposed methodologies. Specifically, this paper reports the first steps that have been carried out in this line of research: choosing the parameters to identify the market segment; identifying the characteristics to include in the procedure for which to determine the respective marginal prices.

From appraisal function to Automatic Valuation Method (AVM). The contribution of International Valuation Standards in modern appraisal methodologies

In the real estate appraisal, as well as for any other scientific discipline, there is a continuous development of knowledge with related theoretical and applicative evolution that takes place primarily on the doctrinal level. The discipline has been object of regulatory and technological improvement aimed at concretizing and defining theoretical principles and methodologies. The functions of value appraisal, formulated in the different procedures that fall within the Market Oriented Approach indicated by the International Valuation Standard, have undergone a process of evolution that has favoured the introduction of Automated Valuation Methods (AVM). This evolution process represents an improvement of the conventional appraisal models that have improved the techniques of data retrieval and data base access and certify the reliability of the models to build efficient evaluation processes and universally recognized. This article aims at reviewing the methodological evolution in relation to the national market approach that the estimation discipline has recorded in recent years by virtue of the drafting and adoption of the International Valuation Standards.

Hierarchical fuzzy regression functions for mixed predictors and an application to real estate price prediction

Categorical features appear in datasets from almost every practice area, including real estate datasets. One of the most critical handicaps of machine learning algorithms is that they are not designed to capture the qualitative nature of the categorical features, leading to sub-optimal predictions for the datasets with categorical observations. This study focuses on a new fuzzy regression functions framework, namely hierarchical fuzzy regression functions, that can handle categorical features properly for the regression task. The proposed framework is benchmarked with linear regression, support vector machines, deep neural networks, and adaptive neuro-fuzzy inference systems with real estate data having categorical features from six markets. It is observed that the proposed method produces better prediction performance for real estate price prediction than the benchmark methods in a wide variety of real estate markets. Since we provide all the required software codes to implement the proposed hierarchical fuzzy regression functions framework, our approach offers practitioners a readily applicable, high-performing tool for real estate price prediction and other regression problems involving categorical independent features.

Mortgage Lending in the Russian Federation

Relevance. Today in Russia, more than half of the population feels the need to improve their housing. However, the opportunity to purchase housing without the help of borrowed funds is available only to a small group of citizens. Mortgage lending is the only way that allows you to purchase your own home and make it affordable for the population in Russia. The purpose of the study is to study the current state of mortgage lending in Russia, in particular, changes in average terms, interest rates on mortgages and leaders in the mortgage lending market.Objectives: to identify the role of mortgage lending in the Russian Federation; reflect the features of mortgage lending in Russia; analyze the mortgage lending market and its participants.Methodology. During the research process, general scientific methods were used: scientific abstraction, analysis and synthesis, generalization of scientific practice, deduction, induction.Results. The study shows that mortgage lending in Russia is actively developing. The availability of mortgages depends on the relationship between the level of income of the population and housing prices. The Russian market is now experiencing high real estate prices and, accordingly, high rates for this type of lending. All these factors hinder the development of the mortgage lending market, which is why the growth rate of mortgage lending has been declining in recent years.Conclusions. The increase in the volume of issued loans largely depends on the weighted average interest rate on mortgage loans. At the beginning of 2023, the weighted average rate on mortgage loans in Russia was 6.65% per annum, which was lower than at the beginning of the previous five years. In 2020, new types of preferential categories were introduced, which had a positive impact on the mortgage market as a whole. From an analysis of data on the issuance of mortgage loans in Russia over the past four years, it is clear that Sberbank and VTB are the leaders in mortgage lending in Russia, controlling most of the market.

Analysis of China's Real Estate Market Volatility and Financial Stability

Fluctuations in China's real estate market profoundly affect the economy, the financial system and social stability. Effective policy regulation, including the adjustment of monetary policy and the control of speculative behavior, is essential to mitigate market volatility and reduce financial risks. This paper uses the historical data analysis method and literature research method to conduct research and analysis based on the study of the meaning of financial stability. Firstly, it divides the historical fluctuation of China's real estate market into four stages and explores the characteristics and causes of real estate price changes in each stage. Thus, the historical fluctuation pattern of China's real estate market and the mechanism by which real estate market fluctuations affect financial stability are derived. The mechanism by which real estate affects financial stability through the personal wealth channel, the balance sheet channel, and the liquidity channel is explored. The findings of this paper can provide a basis for government decision-making and guide real estate practitioners and investors to grasp market fluctuations and avoid potential risks.

Identification and Semiparametric Efficiency Theory of Nonignorable Missing Data with a Shadow Variable

We consider identification and estimation with an outcome missing not at random (MNAR). We study an identification strategy based on a so-called shadow variable . A shadow variable is assumed to be correlated with the outcome but independent of the missingness process conditional on the outcome and fully observed covariates. We describe a general condition for nonparametric identification of the full data law under MNAR using a valid shadow variable. Our condition is satisfied by many commonly used models; moreover, it is imposed on the complete cases, and therefore has testable implications with observed data only. We characterize the semiparametric efficiency bound for the class of regular and asymptotically linear estimators and derive a closed form for the efficient influence function. We describe a doubly robust and locally efficient estimation method and evaluate its performance on both simulation data and a real data example about home pricing.

A Literature Review of the Macroeconomic Implications of Entrepreneurship: A Research Perspective of the Future

This study reviews the literature on entrepreneurship from several angles, including the entrepreneur's point of view, finding opportunities, creating a business plan, securing finance, and entrepreneurship's effect on economic well-being. The authors stress the importance of entrepreneurs in developing novel products and services, expanding economies, and developing new methods of doing things. Employment, economic growth, and tax revenues are all highlighted as key benefits of entrepreneurship. In addition, the article delves into what factors, such as taxes, the price of real estate, the nature of government oversight, the need for funding, and the difficulty of enforcing contracts, make up an entrepreneurial ecosystem. It indicates that a good regulatory environment and tax cuts encourage entrepreneurship. Furthermore, the report offers predictions for the future of entrepreneurship, including an increase in low-tech, socially-minded businesses that concentrate on solving local problems. It suggests further studies be conducted on the relationships between various forms of entrepreneurship and their respective development prospects. The report also emphasizes the need of future research using a wide range of dependent welfare factors, as well as foreign comparative studies on entrepreneurship utilizing various common metrics. It also stresses the need for more study into the link between entrepreneurship and social and environmental progress in underdeveloped nations. In its last section, the research stresses the significance of learning about the factors that influence entrepreneurs and how those factors affect incentive and support programs for entrepreneurs.

The spillover effect of real estate boom on stock market efficiency: evidence from China

ABSTRACT We examine how the increase in real estate prices affects stock market efficiency. Using a sample of Chinese listed firms from 2010 to 2019, we find that high local real estate prices significantly exacerbate stock mispricing. Our findings remain robust after dealing with endogeneity concerns using an instrumental variable approach, an alternative sample choice, and alternative variable measurement. The positive effect of housing prices on stock mispricing is weaker for cross-listed firms and more pronounced for firms with more positive media coverage. Further analysis shows that the booming real estate market accelerates c orporate financial assets investment and inspires optimistic investor sentiment. Our results shed light on the negative spillover effect of the overheated real estate sector from the perspective of capital market efficiency.

Effect of conventional and unconventional monetary policy shocks on housing prices in Canada

This paper investigates the relative importance and effect of conventional and unconventional Canadian monetary policy surprises on housing prices. Using a credible approach to identify structural monetary policy shocks in Canada and a comprehensive Bayesian VAR model to analyze their effects on financial and macroeconomic variables, we find that both conventional monetary policy and quantitative easing shocks have a significant and persistent effect on housing prices in Canada. However, the inflationary effect of a quantitative easing shock is more pronounced than that of an easing target monetary policy surprise. Specifically, the peak effect of a 25-basis point expansionary conventional monetary policy shock is a 2.30% increase in real housing prices while a comparable quantitative easing surprise leads to a peak of 4.56% increase in real housing prices. We conclude that expansionary monetary policy in various forms has had significant inflationary effects on Canadian housing prices. But quantitative easing surprises stand out as the most prominent contributory factor to the escalating Canadian real estate price. Quantitative easing impact consistently outweighs the effects of forward guidance and conventional monetary policy shocks.

A multivariate spatial and spatiotemporal ARCH Model

This paper introduces a multivariate spatiotemporal autoregressive conditional heteroscedasticity (ARCH) model based on a vec-representation. The model includes instantaneous spatial autoregressive spill-over effects, as they are usually present in geo-referenced data. Furthermore, spatial and temporal cross-variable effects in the conditional variance are explicitly modelled. We transform the model to a multivariate spatiotemporal autoregressive model using a log-squared transformation and derive a consistent quasi-maximum-likelihood estimator (QMLE). For finite samples and different error distributions, the performance of the QMLE is analysed in a series of Monte-Carlo simulations. In addition, we illustrate the practical usage of the new model with a real-world example. We analyse the monthly real-estate price returns for three different property types in Berlin from 2002 to 2014. We find weak (instantaneous) spatial interactions, while the temporal autoregressive structure in the market risks is of higher importance. Interactions between the different property types only occur in the temporally lagged variables. Thus, we see mainly temporal volatility clusters and weak spatial volatility spillovers.