Commercial Real Estate Prices Research Articles

Combining machine learning and econometrics: Application to commercial real estate prices

AbstractIn this article, we combine a random effects model with different machine learning algorithms via an iterative process when predicting commercial real estate asset values. Using both random effects and machine learning allows us to combine the strengths of both approaches. The random effects will be used to estimate a common trend, property type trends, location value, and property random effects for properties that sold more than once. The machine learning algorithm will fit the observed characteristics (features) in a complex nonlinear fashion. The model is applied to a small sample of 2652 transactions in Phoenix (AZ) between 2001 and 2021. We only observe a limited number of property characteristics. The average out‐of‐sample MAPE is below 11%, which is as good or even better compared to the average appraisal error found in literature. The out‐of‐sample MAPE is even 9% for properties that sold more than once in the training set. In addition, our model provides indexes and locational heatmaps. These have their own uses and cannot be obtained with standard machine learning algorithms.

Covid-19 Impact to Retail, Hospitality, and Office Space in Malaysia

Abstract The COVID-19 pandemic has disrupted economies and industries worldwide, including the real estate sector. This study aims to assess the effects of the pandemic on commercial real estate prices in the Malaysian market. By examining variations in property types and considering key factors influencing pricing, the research contributes to a better understanding of the pandemic’s impact on the real estate market. To analyze the effects of the COVID-19 pandemic on commercial real estate prices, a mixed-method approach was employed. The study combines data from direct real estate indices, which provide insights into property prices based on transaction data, and listed real estate, which includes publicly traded real estate investment trusts (REITs). By utilizing both sources, a comprehensive analysis of the market is achieved. The sample for this study consists of commercial real estate properties in the Malaysian market. It includes properties from various sectors, such as retail, hospitality, and office buildings. The sample is representative of the overall market and captures the different property types affected by the pandemic. The analysis begins by comparing direct real estate indices to highlight the limitations and potential biases associated with using these indices. It then examines the variations in commercial real estate prices during the COVID-19 outbreak, focusing on the different property types. Statistical techniques, such as regression analysis and trend analysis, are employed to identify patterns and quantify the impact on commercial real estate prices. The study’s main findings reveal that the retail and hospitality sectors experienced the most significant impact on commercial real estate prices during the COVID-19 pandemic. These sectors witnessed a substantial decline in property values due to restrictions, lockdown measures, and reduced consumer demand. Office buildings, although moderately affected, also experienced some decline in prices. This research contributes to the existing literature on the effects of the COVID-19 pandemic on commercial real estate prices, specifically in the Malaysian market. By combining data from direct and listed real estate sources, the study provides a comprehensive understanding of the variations in property prices across different sectors. The findings offer valuable insights for real estate investors, policymakers, and industry professionals in adapting to the changing market conditions and making informed decisions regarding commercial real estate investments. In conclusion, this article sheds light on the effects of the COVID-19 pandemic on commercial real estate prices in the Malaysian market. The research methodology, which combines data from direct and listed real estate, allows for a comprehensive analysis of property variations among different sectors. The findings emphasize the significant impact on the retail and hospitality sectors, while showing office buildings to have been moderately affected. It also provides important insights for stakeholders in the real estate industry, enabling them to make informed decisions and develop appropriate strategies.

The Influence of Virtual Events on Metaverse Commercial Real Estate Values: A Review

Purpose: This review paper examines the complex relationship between virtual events and the commercial real estate prices in the dynamic Metaverse, which is a network of interconnected virtual communities enabling real-time interactions. Virtual events have emerged as a prominent element of our digital reality, surpassing geographical limitations and reshaping the understanding of interaction and professional connections. This paper aims to explore the intricate interplay among technology, virtual events, and the commercial real estate ecosystem within the Metaverse.
 Materials and Methods: It will analyse several case studies, identify emerging trends, and assess the economic ramifications of these dynamics. This observation highlights the capacity of virtual events to influence the real estate environment of the Metaverse, presenting prospects for developers and investors. However, it also underscores the need for comprehensive regulatory frameworks to guarantee equity and long-term viability.
 Findings: The increasing recognition of virtual events by corporations and organisations has led to a growing need for appropriate virtual venues inside the Metaverse. Consequently, this surge in demand has directly influenced the worth of virtual commercial real estate. The ongoing development of the Metaverse holds the potential to create a forthcoming reality in which the physical and virtual domains coexist and fundamentally transform our methods of engaging with the surrounding environment.
 Implications to Theory, Practice and Policy: Utilise digital twin technology for interactive property replicas, enabling potential buyers to explore properties virtually before visiting. Enhance efficiency and transparency in property transactions by exploring blockchain and smart contracts; leverage the metaverse for collaborative design and conduct virtual training for real estate agents to optimize technology utilisation.

Commercial real estate prices in Europe after COVID-19

PurposeThe article aims to analyze the behavior of commercial real estate prices in Europe, with a focus on the post-coronavirus disease 2019 (COVID-19) pandemic period. The authors use national and city-level data for the various commercial real estate sectors in ten countries, as well as listed real estate data, to assess any differences across property type and space.Design/methodology/approachThe authors analyze the behavior of commercial real estate prices after the COVID-19 pandemic, emphasizing differences across property types. For that purpose, the authors use national and city-level direct real estate data for the ten largest countries in terms of market capitalization, as well as listed real estate data. The article then turns to discussing the likely trajectory of commercial real estate prices in the future.FindingsThe recent rise in interest rates and geopolitical instability have affected prices differently across sectors. Industrial properties benefited from the pandemic, although prices declined significantly in 2022. Residential properties continued their upward price trend and have been the best-performing property type during the last two decades. Retail real estate continued its downward price trajectory. Thus far, office markets do not appear to be significantly affected by structural changes in the sector. The data for listed real estate markets in Europe suggest that markets bottomed out in early 2023.Originality/valueThis paper provides for a better understanding of the behavior of commercial real estate prices in Europe since the COVID-19 pandemic. The authors assess whether the effects found during the COVID-19 crisis were temporary or long-lasting. Also, many economic and political uncertainties have emerged since the beginning of the Ukraine war in February 2022, and it is important to analyze the effects of such uncertainties on commercial real estate prices.

지역별 산업동향이 지역별 상가가격에 미치는 영향에 관한 연구

지역별 산업동향이 지역별 상가가격에 미치는 영향에 관한 연구

Price diffusion across international private commercial real estate markets

We explore spatio-temporal aspects of global commercial real estate price movements and consider two channels where prices may spill over between global cities: (i) through a dominant market and (ii) through “neighbouring” markets. Neighbouring, here, is defined as the degree of overlap in ownership. We document significant ripple effects from both channels in commercial real estate prices across 22 markets from 2007 to 2020. In particular, London is found to be the dominant market and price shocks significantly diffuse across other global cities in the short- to medium-run. Additionally, shocks from neighbouring markets are important in the short- to medium-run. In the long-run, macroeconomic factors play a much more critical role. The spillover effect through both channels is more predominant during the financial crisis. In fact, the dominant market channel is mostly driven by the financial crisis. By contrast, the neighbouring market channel is significant throughout the economic cycle.

COMMERCIAL REAL ESTATE PRICES IN MALAYSIA AND COVID-19

The consequences of the coronavirus disease 2019 (COVID-19) pandemic on commercial real estate pricing are examined in this article, with a focus on the Malaysian market. It begins by pointing out certain cautions to keep in mind while using direct real estate indices. The authors then look at how commercial real estate prices changed during the epidemic, highlighting disparities between property kinds. This study uses data from both direct and listed real estate to achieve this goal and goes on to explore changes in the primary elements driving commercial real estate pricing. The essay then moves on to the expected future trajectory of commercial real estate values. Retail and hospitality assets, as well as, to a lesser extent, office buildings, were shown to be the most affected by the findings.

Commercial Real Estate Prices in Europe After COVID-19

Commercial Real Estate Prices in Europe After COVID-19

Combining Algorithmic and Stochastic Data Models: Application to Commercial Real Estate Prices

Combining Algorithmic and Stochastic Data Models: Application to Commercial Real Estate Prices

Closed for mafia: Evidence from the removal of mafia firms on commercial property values

AbstractUsing a sample of almost 7000 Italian municipalities from 2002 to 2019, we investigate how the removal of mafia‐infiltrated firms affects commercial sale and rental prices. We conjecture that targeting mafia businesses leads to a reduction in local disamenities and an increase in the demand for commercial properties. Applying the latest methodologies based on difference‐in‐differences approaches, we show that antimafia policies aimed at confiscating and reassigning mafia firms have positive spillover effects on commercial real estate prices, driving values upward by about 4%. This is especially true for small–medium municipalities in mafia‐ridden provinces. The evidence we provide supports the enforcement of antimafia policies with an economic content, as they pose a threat to criminal financial interests and curb the mafia influence in the market.

Comovements and spillovers in international commercial and residential real estate markets

PurposeThis study aims to know to what extent do the commercial and residential estate markets move together in different economies? Do the shocks originating in one of these markets spillover to the other markets?Design/methodology/approachThe authors apply a modified version of the dynamic factor model to commercial and residential real estate prices in the Euro area, Hong Kong, Singapore and the USA. This modified dynamic factor model decomposes price growth in these two real estate markets into common, spillover and idiosyncratic components.FindingsThe results show significant heterogeneity in the relative importance of different components in the evolution of commercial and residential price growth across different economies. The findings suggest that the spillover from the residential to commercial real estate market dominates the spillover from the commercial to real estate market for all the economies in our sample. The authors also find that the common component accounts for a large fraction of the price movements in the residential markets in the European Union (EU) area and the USA, whereas spillover and common components together explain more than two-thirds of the variations in Hong Kong and Singapore. The results suggest that the role of spillover from one market to another increased significantly during the financial crisis of 2008–2009.Originality/valueThis paper contributes to the existing literature on how the transmission of shocks takes place across commercial and residential real estate markets. The transmission of shocks can take place in two directions in the proposed framework. There may be a direct spillover from a shock from one market to another. This corresponds to a shock to the idiosyncratic component affecting the other idiosyncratic component. In this paper, the authors are mainly interested in indirect spillover where the shock would transmit from the idiosyncratic factor to the common factor, and then from the common factor to the other idiosyncratic factor.

Commercial real estate prices and COVID-19

PurposeThe article analyzes the effects of the coronavirus disease 2019 (COVID-19) pandemic on commercial real estate prices, with a particular focus on European markets.Design/methodology/approachThe authors start by highlighting caveats to bear in mind when referring to direct real estate indices. The authors then analyze the behavior of commercial real estate prices during the pandemic, emphasizing differences across property types. For that purpose, the authors use data for both direct and listed real estate and further discuss changes in the main factors affecting commercial real estate pricing. The article then turns to discussing the likely trajectory of commercial real estate prices in the future.FindingsThe authors report that retail and hospitality properties and to a lesser extent office buildings have been affected the most by COVID-19, while the residential and industrial sectors have been less affected by the crisis. The authors maintain that the future trajectory of prices will vary across sectors and that the type and location of assets will become increasingly important in their valuation.Originality/valueThis paper provides for a better understanding of the behavior of commercial real estate prices during the COVID-19 pandemic.

The financial impact of street-level greenery on New York commercial buildings

Urban street-level greenery is empirically documented to improve mental and physical health, increase productivity, increase urban environmental equality and reduce carbon footprints. In addition, these benefits raise residents’ welfare, which has been correlated with increases in residential house prices. We measure street-level greenness in New York City through a novel Green View Index (GVI) using Google Street View images, and assess the impacts of greenness on commercial real estate prices. Using a sample of office transactions, we spatially correlate Google Street View Images for New York City over the 2010 to 2017 period. We find an 8.9% to 10.5% statistically, economically and positive transaction premium and a 5.6% to 7.8% rent premium for offices with low to high street-level greenness relative to those building transactions spatially correlated with very low greenness. Estimations are robust t with proximity to parks, subway stations, sidewalk widths, household income levels and investments by Building Improvement Districts, as well as other vital and standard office valuation features. By documenting the role of greenery in commercial building valuations, our results give a more complete understanding of the value of greenness in urban environments, as well as the economic role that urban landscape architecture, planning and development has upon cities.

What influences real estate volatility in Hong Kong? An ARMA-GARCH approach

PurposeThis paper aims to examine real estate price volatility in Hong Kong. Monthly data on housing, offices, retail and factories in Hong Kong were analyzed from February 1993 to February 2019 to test whether volatility clusters are present in the real estate market. Real estate price determinants were also investigated.Design/methodology/approachAutoregressive conditional heteroscedasticity–Lagrange multiplier test is used to examine the volatility clustering effects in these four kinds of real estate. An autoregressive and moving average model–generalized auto regressive conditional heteroskedasticity (GARCH) model was used to identify real estate price volatility determinants in Hong Kong.FindingsThere was volatility clustering in all four kinds of real estate. Determinants of price volatility vary among different types of real estate. In general, housing volatility in Hong Kong is influenced primarily by the foreign exchange rate (both RMB and USD), whereas commercial real estate is largely influenced by unemployment. The results of the exponential GARCH model show that there were no asymmetric effects in the Hong Kong real estate market.Research limitations/implicationsThis volatility pattern has important implications for investors and policymakers. Residential and commercial real estate have different volatility determinants; investors may benefit from this when building a portfolio. The analysis and results are limited by the lack of data on real estate price determinants.Originality/valueTo the best of the authors’ knowledge, this paper is the first study that evaluates volatility in the Hong Kong real estate market using the GARCH class model. Also, this paper is the first to investigate commercial real estate price determinants.

Commercial Real Estate Prices and Covid-19

The article analyzes the effects of the COVID-19 pandemic on commercial real estate prices. We start by highlighting caveats to bear in mind when referring to direct real estate indices. We then analyze the behavior of commercial real estate prices during the pandemic, emphasizing differences across property types. For that purpose, we use data for both direct and listed real estate. We further discuss changes in the main factors affecting commercial real estate pricing. The article then turns to discussing the likely trajectory of commercial real estate prices in the future. We report that retail and hospitality properties and to a lesser extent office buildings have been affected the most by COVID-19. The other sectors, in particular the residential and industrial sectors, have shown more resilience. We maintain that the future trajectory of real estate prices will vary across sectors and that the type and location of assets will become increasingly important in their valuation.

Commercial Real Estate and Macrofinancial Stability During Covid-19

The COVID-19 pandemic crisis has severely shocked the commercial real estate (CRE) sector, which could have important implications for macro-financial stability going forward because of the large size of the sector and its strong interconnectedness with the real economy. Using a novel methodology, this paper quantifies vulnerabilities in the CRE sector and analyzes policy tools available to mitigate related risks. The analysis shows that CRE prices were overvalued in several major advanced economies in 2020:Q1. It also shows that such price misalignments increase the likelihood of future price corrections and exacerbate downside risks to future GDP growth. While the path of recovery in the sector will depend inherently on the pace of overall economic recovery and the structural shifts induced by the pandemic, easy financial conditions may contribute to an increase in financial vulnerabilities and persistent price misalignment. Macroprudential policy can, however, be effective in curbing the financial stability risks posed by the CRE sector.

Hedging Commercial Real Estate Price Risk: Evidence from the U.S.

Hedging Commercial Real Estate Price Risk: Evidence from the U.S.

A Machine Learning Approach to Price Indices: Applications in Real Estate

This paper proposes a methodology for using machine learning regression models to create price indices. In our study we developed six commercial real estate price indeces for the city of New York from year 2000 to 2019. The regression models used in this study are eXtreme Gradient Boosting Tree (XGBT), Support Vector Regression (SVR) and averaged Neural Networks (avNNet). The benchmark for comparison of the results is Ordinary Least Squares (OLS). There are two main index methodologies, a chained, where the index is constructed using out-of-sample data, and a pooled, where the index is constructed using in-sample data. The two main index methodologies can be divided into sub categories that utilizes a Paasche and Laspeyres like index formulations. Another important factor included in this study is the size, in years, of the optimal training window for building the index. The results show that the machine learning approach produced, overall, lower estimation errors. Nevertheless, these lower estimation errors not always constitutes a more stable index. Additionally, all models, including the benchmark, are sensitive to the training window size regarding the out-of-sample estimation error.

The Financial Impact of Street-Level Greenery on New York Commercial Buildings

Urban street-level greenery is empirically documented to improve mental and physical health, increase productivity, increase urban environmental equality and reduce carbon footprints. In addition, these benefits raise residents' welfare, which has been correlated with increases in residential house prices. We measure street-level greenness in New York City through a novel Green View Index (GVI) using Google Street View images and assess the impacts of greenness on commercial real estate prices. Using a sample of office transactions, we spatially correlate Google Street View Images for New York City over the 2010 to 2017 period. We find a 8.9% to 10.5% statistically, economically and positive transaction premium and a 5.6% to 7.8% rent premium for offices with low to high street-level greenness relative to those building transactions spatially correlated with very low greenness. Estimations are robust to proximity to parks, subway stations, sidewalk widths, household income levels and investments by Building Improvement Districts, as well as other vital and standard office valuation features. By documenting the role of greenery in commercial building valuations, our results give a more complete understanding of the value of greenness in urban environments, and the economic role that urban landscape architecture, planning and development has upon cities.

Imperfect Substitutability in Real Estate Markets and the Effect of Housing Demand on the Macroeconomy

Changes in housing demand can have a macroeconomic effect through the collateral channel, where the change in residential real estate prices is associated with a change in commercial real estate prices, affecting firm collateral and thus firm investment. We argue that this channel is weaker when residential and commercial real estate are poor substitutes. Using cross-state heterogeneity in the strength of zoning regulations as a proxy for heterogeneity in the substitutability of residential and commercial real estate, we first show with firm level data that the strength of local zoning regulations has a negative effect on the estimated increase in firm investment following an increase in local residential real estate prices. We then construct a DSGE model where land has both residential and commercial uses and estimate it using Bayesian techniques and U.S. macroeconomic data. We find the average elasticity of substitution between commercial and residential real estate in the U.S. to be around 0.35, but in states with strong zoning restrictions it can be as low as 0.16 and in states with weak zoning restrictions it can be as high as 0.66. Simulations of the model show how these differences in zoning restrictions can affect the transmission of a housing demand shock to the macroeconomy.