Real Estate Research Articles

The relative importance of economic policy uncertainty and geopolitical risk on U.S. REITs returns

PurposeOur aim in this study is to investigate the relative importance of the economic policy uncertainty and of the geopolitical risk on U.S. REITs (Real Estate Investment Trusts) returns with a special focus on the different real estate sectors.Design/methodology/approachWe use an augmented Fama-French (1993)’s asset pricing model, including economic policy uncertainty indices (EPU), introduced by Baker et al. (2016), and geopolitical risk indices (GPR) recently developed by Caldara and Iacoviello (2022), to price the potential risk factors for U.S. Nareit indices returns. To obtain robust economic results, we correct for the problems of errors-in-variables in linear asset pricing models; we advocate the use of higher moments estimators as instruments in a generalized method of moments (GMM) framework.FindingsOur results report that economic policy uncertainty (EPU), and geopolitical risk (GPR) are priced for the different Nareit sectors for the last three decades. The GPR index stands as a relevant risk factor. The coefficient estimates are low compared to Fama-French risk factors. They are higher for Shopping Centers, Retail and Region Malls and lower for Health Care and Lodging/Resorts. EPU indices are also priced and less statistically significant. Health Care sector, followed by Shopping Centers and Retail are the most policy-sensitive sectors.Practical implicationsIn their “2023–2024 Top Ten Issues Affecting Real Estate” “political unrest and global economic health” is ranked 1 issue by the Counselors of Real Estate. Our results report that economic policy uncertainty and geopolitical risk are priced for the different Nareit sectors. They suggest implications for investors, insurers, bankers, policymakers and other stakeholders. The geopolitical risk index (GPR) stands as a relevant and significant risk factor for REITs returns.Originality/valueBased on parsimonious robust asset pricing models, the results shed a new light on the relative importance of geopolitical risk and economic policy uncertainty in the real estate sector, with a special focus on the different U.S. REITs sectors. They suggest possible implications for investors, insurers, bankers, policymakers and other stakeholders in a context marked by higher uncertainty shocks and geopolitical risks.

Planners Becoming Visualizers in the Mediatized World: Actor-Network Analysis of Cairo’s Street Billboards

While visual communication is crucial in urban planning, there is a gap in understanding how dominant narratives and visuals affect professional planning practice and planners’ roles, particularly in mediatized urban environments. This study addresses this gap by examining street billboards in Cairo to understand how planning visualizations contribute to the restructuring of the planning profession. It explores how these visual tools shape the practice and roles of urban planners, who are increasingly becoming visualizers. Employing actor-network theory, the study traces the relationships between billboards, planners, and other network actors. The primary research question is: How and why does the use of planning visualizations (billboards) restructure the profession of planning, including planning practice and the roles of planners? Utilizing a qualitative exploratory methodology, the study focuses on billboards along Cairo’s 6th of October Bridge. Data were analyzed through visual and content analysis of 209 billboards to understand their language, content, patterns, and geo-positioning. The analysis revealed that billboards in Cairo significantly impact urban landscapes and the visual culture of urbanization, often promoting exclusive real estate projects to a socio-economic elite. The research highlights the dilemmas in the changing professional roles of planners within a mediatized world and underscores the need for more inclusive planning practices. By employing actor-network theory, the study provides a nuanced understanding of the complex relationships that shape and are shaped by the visual culture of urban planning, offering insights into how planners can navigate and influence these dynamics for more equitable urban development.

Exploring the impact of renovation subsidies on housing markets – evidence from the Swedish property market

PurposeThe primary aim of this research is to examine the effects of the Renovation, Conversion, and Extension (ROT) tax deduction for renovations on the scope and quality of renovations and its subsequent impact on house prices across various Swedish municipalities.Design/methodology/approachThis study utilises a two-way fixed effect instrument variable (IV) spatial Manski approach, analysing balanced panel data from 2004 to 2020 at the municipal level (290 municipalities) in Sweden. The methodology is designed to assess the impact of the ROT subsidy on the housing market.FindingsThe study reveals that the ROT subsidy has significantly influenced house prices, with noticeable variations between municipalities. These differences are attributed to the varying amounts of tax reductions for renovations and the extent to which property owners utilise these subsidies.Research limitations/implicationsThe research is limited to the context of Sweden and may not be generalisable to other countries with different housing and subsidy policies. The findings are crucial for understanding the specific impacts of government subsidies on the housing market within this context.Practical implicationsFor policymakers and stakeholders in the housing market, this study highlights the tangible effects of renovation subsidies on property values. It provides insights into how such financial incentives can shape the housing market dynamics.Social implicationsThe research underscores the role of government policies in potentially influencing equitable access to housing. It suggests that subsidies like ROT can have broader social implications, including the distribution of housing benefits among different income groups and regions.Originality/valueThis study contributes original insights into the field of applied real estate economics by quantitatively analysing the impact of a specific government subsidy on the housing market. It offers a unique perspective on how fiscal policies can affect property values and renovation activities at the municipal level in Sweden.

Optimal location to land banking practices in urban-rural informal land market continuum of Ghana

Land banking practices in complex informal land markets are growing in developing countries. However, the land banking (LB) literature predominantly focuses on publicly driven land banks operating within formal land markets. Against this backdrop, this study investigates optimal locations for LB projects in Ghana's complex informal land markets from the perspective of private and semi-public real estate developers. Utilising a two-stage research process, first, the study developed a conceptual framework by using: (1) suppositions regarding space under economic geography; and (2) theoretical suppositions on the use of LB and its influence on LB locational choices uncovered from an interpretive hermeneutic literature review. The second stage focused on an empirical assessment of the conceptual framework by taking four urbanised regions in Ghana. The case study stage uses primary data from 30 interviewees selected using purposive and snowball sampling, while secondary data comprised land bank inventories from the regional Lands Commissions of the case study regions. Results revealed land title security as the primary factor determining optimal locations for land banks. There are significant challenges related to land title security in urban and inner parts of peri-urban areas. These challenges are aggravating the transformation of agricultural lands into residential lands in developers' preferred land bank locations. Based on the ongoing land transformation occurrences, the study underscores the need for policy responses that enhance title security to encourage developers to diversify their land banking locational preferences beyond solely greenfield sites to a mix of green and urban brownfield sites.

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.

The Effect of the Evergrande Bankruptcy on Chinese Real Estate Listed Firms

The objective of the study is to examine the intra-industry effects of Evergrande’s bankruptcy on the Chinese real estate listed firms. Based on an event study, we evidence a negative and statistically significant stock price reaction to Evergrande’s bankruptcy announcement. These results are consistent with the contagion effect. We also find the highest negative impact on real estate firms with greater leverage and a higher similarity in cash flows with the bankrupt firm. Finally, the magnitude of the stock market reaction to Evergrande’s bankruptcy is reinforced or mitigated by firm-specific determinants such as size and liquidity.

How Does Digital Infrastructure Development Affect Housing Prices? A Quasi-Natural Experiment Based on the “Broadband China” Program

Currently, digital infrastructure development, as a key driver of economic growth in the new era, provides a new perspective for alleviating the current tremendous downward pressure on China’s real estate market. Based on data from 255 prefecture-level cities in China from 2005 to 2020, this study takes the “Broadband China” Program as a quasi-natural experiment and uses the time-varying difference-in-differences (DID) model to investigate mechanisms of impact of digital infrastructure development on housing prices. The empirical results show that the “Broadband China” Program has a significant positive impact on housing prices, with an average treatment effect of 8%. This primarily occurs through population agglomeration, income enhancement, and technological innovation. It has also a positive spatial spillover effect on housing prices. Overall, digital infrastructure development is a “double-edged sword”: while improving the quality of life and convenience, it will also bring about rising housing prices. Therefore, we should pay more attention to how to guide digital infrastructure development, including broadband networks, to reasonably regulate housing prices to promote the healthy development of China’s real estate market.

Analysis of Japan's Real Estate Regulatory Policies and Their Effects

The development of real estate is closely related to China's national economy, and in recent years, China's real estate industry has experienced rapid development and change. With the continuous expansion of the industry's development scale and the increasingly complex market environment, the real estate industry is also facing a series of challenges and problems. Japan also faced the bursting of its real estate bubble in the 1990s. Reviewing the history of the bursting of the real estate bubble in Japan in the 1990s has important research and mirror value for us to clarify the formation and bursting of real estate risks and how to implement appropriate policies. This study analyzes the negative and positive impacts of policies implemented before the bursting of the real estate bubble, and the policies introduced by the Japanese government to address the bursting of the real estate bubble. Based on the results of the analysis of Japan real estate, suggestions are made on the idea of regulating China's real estate industry, the means of regulation and the rhythm of regulation.

From Administrative Price to Market Value of Real Estate. The Evolution of the Valuation System in Poland

Abstract The article applies the method of historical research, using a temporal perspective dating back to 1985, to trace the evolution of market valuation principles for real estate in Poland. These principles evolved similarly to those in many other post-socialist countries, influenced by political and socio-economic transformations and the resulting list of objectives for which these values became essential. The changing legal regulations allowing for the emergence and development of a free real estate market played a decisive role in this process. It was also a period of preparing real estate valuation professionals to meet these requirements. Today, the methodology of valuation, under increasing pressure from various real estate market entities and the rapid advancement of intelligent data collection and processing technologies, is undergoing further evolution. In many countries, including Poland, lively discussions and disputes are ongoing regarding the legal authorization of statistical tools and automated valuation models in valuation practice. These possibilities are being considered particularly in the context of mass property valuations for tax purposes. The methodology involves the analysis of Polish legal provisions, foreign literature, and documents proving the gradual marketization of valuation principles.

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.

The subjective side of real estate fraud

The article deals with the subjective side of the criminal offense under Article 190 of the Criminal Code of Ukraine, in particular, real estate fraud. The author establishes that the subjective side of a criminal offense is of great importance for characterizing fraud. As is known, the subjective side of a criminal offense is its internal side, namely, the mental activity of a person which reflects his/her attitude to the socially dangerous act which he/she commits and its consequences. It is noted that the content of the subjective side of the criminal offense of real estate fraud includes such features as guilt, motive and mercenary purpose. The author concludes that when committing real estate fraud, a person realizes the socially dangerous nature of the offense which he/she commits. Guilt in committing real estate fraud is characterized by direct intent, the content of which is formed by two aspects – intellectual and volitional. The mercenary purpose in committing real estate fraud is expressed in obtaining personal property benefits and is a necessary subjective feature of this type of fraud; it arises in the mind of the guilty person in the form of the final result of the criminal offense – the achievement of material benefit. The motives for committing real estate fraud are of a mercenary nature. They are manifested as internal motivations that guide the perpetrator in committing real estate fraud and form the structure of criminal mercenary motivation. Real estate fraud is committed by persons who are psychologically prepared to commit a criminal offense, clearly realize the illegal actions aimed at seizing ownership of residential property, clearly realize the illegal actions aimed at seizing ownership of someone else’s real estate (to obtain money in a particularly large amount), and the consequences in the form of harm to the victim. Persons who develop such fraudulent schemes mislead victims about their true criminal intentions or abuse the victim’s trust for material gain. The criminals are certainly guided by a selfish purpose and mercenary motives that are fundamental to their behavior.

Transformative Dynamics of the Economy of Kosovo: A Perspective on History and Development of Foreign Direct Investment

Abstract This paper presents an in-depth overview of foreign direct investments (FDI) in Kosovo's economy over more than twenty years. FDI has played a central role, proving indispensable to the development of Kosovo's economy. Thus, FDI is primarily directed towards economic activities like real estate, finance, manufacturing, construction, etc. In terms of the legal framework, Kosovo has approved a range of regulations and laws with the primary goal of protecting, attracting and encouraging foreign investors to invest. However, obstacles such as political instability, energy insecurity, delayed EU integration, and regional political tensions are recognized as the main barriers affecting FDI in Kosovo. Finally, Kosovo should develop suitable policies to boost its investment competitiveness, attract multinational enterprises for investment opportunities, and prioritize less attractive economic sectors for investment of FDI.

The Potential Impact of Blockchain on Real Estate: Insights from Conventional Mortgage Market Data

The Potential Impact of Blockchain on Real Estate: Insights from Conventional Mortgage Market Data

Integrating Real Estate Investment Environment Indicators with Sustainable Urban Development: An Evaluation Framework for Egyptian new cities

Abstract The decision-making process in real estate investment is strongly linked with economic development and the real estate market, exerting a significant influence on the construction industry. In Egypt, decision-makers often prioritize responding to investment pressures to attract investors and establish projects, neglecting the alignment of investments with the principles of sustainable urban development. This study aims to identify the indicators of real estate investment environment according to the sustainable urban development, enhancing the decision-making process in the urban development of new Egyptian cities. Additionally, the research seeks to show best practices and successful global initiatives that effectively integrate real estate investment environment with sustainable urban development, emphasizing on innovative strategies and policies. The study will conclude indicators that harmonize economic progress with spatial, environmental, and social sustainability to enhance decision-making processes in urban development by adopting a more holistic and inclusive approach.

More than one way to peel an orange : A fresh approach to building an effective lease administration function

This paper explores the evolving landscape of lease administration, prompted by shifts in labour markets, growing complexities of financial reporting for lease agreements and the emergence of robust lease management technology. Lease administration emerges as a critical business function, with a focus on managing lease data, ensuring accurate rent payments and navigating compliance and landlord relationships, while providing realtime reporting capability for business stakeholders. In addition, lease administrators transition from labour-intensive manual processes to a more analytical and technical approach. Given the altered environment in lease management requirements, tools and skill sets, now is an ideal juncture for companies to assess their current lease administration operating model, and the paper introduces a perfect assessment tool. The Lease Administration Framework, a proactive lease administration management system, highlights strategies and decisions for managing a company’s real estate portfolio while considering the use of in-house teams and outsourced service providers. The paper advocates for a tailored approach to be aligned with organisational needs, while emphasising the importance of evaluating talent, technology and risk tolerance in the decision-making process. By following the framework, corporate real estate (CRE) leaders can ensure financial prudence, gain the agility to allocate resources strategically, invest in growth initiatives and weather economic uncertainties.

Multiple Machine Learning Models in House Price Prediction: Performance Evaluation and Comparison

House pricing is one of the most critical concerns for people. Accurately predicting house prices is essential for participants and investors in the real estate market. This study delves into the realm of predictive modeling for house prices using a multimodal approach in machine learning. The research methodology spans various stages including data collection, preprocessing, feature engineering, and model selection. Based on a dataset sourced from Kaggle, housing data from Iowa comprising 79 pertinent features influencing property prices is utilized. Performance evaluation is conducted using the root mean square error (RMSE), comparing the efficacy of linear regression models such as Lasso regression, Kernel Ridge regression, and ElasticNet regression against tree-based models including XGBoost, Gradient Boosting, and LightGBM. Additionally, the study implements two stacked ensemble models to identify the most optimal predictive model for house prices. Experimental procedures involve training and evaluating these models on specific datasets to gauge their predictive accuracy. Findings and discussions underscore the comparative analysis of diverse machine learning models within the context of house price prediction. Moreover, the study employs the Gradient Boosting method in machine learning to pinpoint the ten most influential features impacting house prices. Recommendations for future research are provided to enhance the precision and robustness of house price prediction models. This research contributes to the advancement of predictive modeling in the real estate domain, offering insights into the efficacy of various machine learning techniques for forecasting property prices.

Piloting: Adopting a prototype mindset for today’s workplace

This paper is designed to help corporate real estate (CRE) leaders adopt a ‘learning by doing’ approach to workplace strategy and design to improve employee experiences and organisational outcomes. While the COVID-19 pandemic has brought lingering uncertainty relative to the future of the physical office, it has also spawned a spirit of openness to change and a willingness to experiment. In this paper, we discuss ways to learn by doing at various scales, with a focus on the largest-scale methodology: piloting. We show how pilots provide a low-risk approach to introduce flexible work policies and new ways of working within a sustainable financial model. We demonstrate that by generating data and insights, pilots can drive plans to scale and inform future space types within the larger real estate portfolio. This paper draws upon years of research and exploration conducted by MillerKnoll brands and our customers on pilots of varied types and scales. It provides a roadmap to implement a pilot, including questions to ask, selection criteria for teams, and locations and methodologies to measure success. Case studies illustrate various approaches and results achieved.

The Impact of Housing Price Volatility on Household Consumption in Northeast China Across Different Demographic Profiles

Amidst the backdrop of an economic downturn, urban housing prices in China have exhibited substantial volatility, significantly impacting domestic demand and household consumption. Demographic characteristics of households have played a pivotal moderating role in this dynamic. Due to economic stagnation and population outflow, Northeast China has garnered heightened attention. This study employs microdata from households in Liaoning and Heilongjiang provinces, spanning from 1989 to 2015, to conduct an empirical examination. The findings indicate that rising housing prices exert a pronounced positive influence on household consumption, particularly among households with children and those headed by elderly individuals. Conversely, the educational attainment of the household head does not exhibit a statistically significant impact on the real estate wealth effect. The study concludes with policy recommendations aimed at stimulating domestic demand and stabilizing housing prices in Northeast China.

House Price Prediction Taking Account of the Influence of COVID-19

Machine learning algorithms are utilized in various practical applications and academic research. Due to the advent of digital technology, there is now a greater availability of massive, organized, and georeferenced datasets. This has simplified the use of algorithms for trend analysis and detection as well as forecasting, which helps users make decisions. The purpose of this study is to determine which Machine Learning (ML) algorithms are most effective in projecting housing price and to assess how the COVID-19 has affected these prices. The method includes the stages of data preprocessing, feature manipulation, hyperparameter tuning, evaluation, selection, and, in the end, interpretation of the model. Another objective is to assess and compare several ensemble learning strategies, comprising bagging techniques like random forest, and boosting techniques like Gradient Boosting Regressor, using a linear regression model. The results of this study confirm that the real estate sector is very resistant to pandemics, as the decline in prices was not as severe as initially anticipated. Among the several models that were examined, extreme gradient boosting demonstrated the highest level of accuracy, albeit with a minimal difference.

Research on the Sustainability of Property Insurance under the Background of Extreme Weather Events

In recent years, the frequency of extreme weather events has significantly increased, resulting in global losses exceeding $1 trillion. The profit crisis of insurance companies and the issue of homeowners' payment ability have become more apparent. This article focuses on the sustainability of property insurance under extreme weather events, establishes a comprehensive insurance and protection model, and provides recommendations for relevant departments.Firstly, a set of seven indicators including the proportion of household insurance expenditure was selected to establish an indicator system. After conducting Spearman correlation coefficient testing on the data and combining it with LSTM neural network to predict the frequency of extreme weather in the next decade, this paper uses the SVM binary classification model to classify high-risk areas as uninsurable areas and other areas as insurable areas. This decision-making process resulted in insurance companies covering areas such as Alabama and refusing to underwrite insurance in Arizona, while also making relevant recommendations.Secondly, by calculating indicators for communities and real estate developers, and considering the impact of population growth on housing demand, this article uses the ARIMA model to predict population growth in different regions over the next five years. After standardizing the data, the entropy weighting method is applied to consider the subjective importance of different real estate developers for different indicators and assign weights to the indicators. Weighted indicator data is used as input for the SVM model, dividing regions into two categories, with regions with better overall conditions being more favorable for real estate development. This has led people to prioritize the development of regions such as North Carolina and Indiana. In addition, this article introduces the TOPSIS regional ranking comprehensive evaluation index, which is beneficial for the development of higher ranked regions. Similar to our SVM classification results, Indiana and North Carolina rank second and third.