Commercial Property Price Research Articles

The non-linear impact of urban rail transit on commercial property prices: Based on a spatial heterogeneity perspective

Urban rail transit (URT) can boost population and business agglomeration, and increase premiums for surrounding properties. Commercial properties with an operational nature typically gain higher premiums and have the potential to capture value. However, the relationship between URT and commercial property price (CPP) has not been fully explored. Therefore, this paper takes Shanghai as an example, based on URT travel data, property sales information, and other multi-source geographic data, comprehensively analyses the intrinsic mechanism of URTs affecting CPPs. The results confirm that URTs have a significant positive effect on CPPs, but the degree of impact varies in the presence of spatial heterogeneity and submarket effects. On the urban scale, the effect increases and then decreases as one moves away from the CBD; at the station area scale, URTs have a non-linear step-decreasing effect on CPPs. Furthermore, the premium that URTs can generate is limited and subject to multiple constraints in terms of location, built environment, residents' travel habits and expectations. The findings can provide a reference for formulating value capture policy, planning URT routes, and enhancing urban economic vitality.

A New Perspective on Real Estate in Thailand in the Post-Epidemic Era: Multi-Criteria Decision Analysis

The objective of this study was to evaluate the impact of the pandemic on Thailand’s real estate market and to propose recommendations for investors and other market participants on how to continue operating in the market. The primary research methods employed were analysis, forecasting, and abstraction. The impact of the global pandemic on Thailand and the country’s economy, with a particular focus on the real estate sector, was evaluated. The changes observed in the real estate sector since the onset of the pandemic were presented, including a decline in activity due to travel restrictions, a correction of residential and commercial property prices, an increase in demand for suburban properties, and a shift towards online sales, among other notable developments.

Development of the Capital Value Index of Commercial Real Estate on the Czech Market

Abstract The global financial crisis has shown that property prices can significantly affect the financial sector and the real economy. More multinational investors are active in the commercial real estate market compared to the residential sector, which, according to the European Central Bank, increases the risk of spill-over of financial instability from abroad. Although the evolution of commercial property prices may increase the probability of default of firms operating in this sector, more attention is not given to a detailed analysis, which focuses mainly on residential property. This article analyses the development of the commercial real estate capital value index in the Czech Republic. The analysis revealed that, unlike the global economic crisis, the COVID-19 pandemic had a different impact on individual commercial real estate markets.

Forecasting commercial real estate indicators under COVID-19 by adopting human activity using social big data

Dependence of the real estate sector on human activity has been unveiled during the COVID-19 pandemic. In addition, it is assumed that trends emitted from the location-based social networks (LBSNs) successfully reflect human activities, hence commercial property trends. This study examined the use of social media to forecast commercial real estate figures during COVID-19 in Istanbul and determined the potential of social media data for forecasting the future rent/price levels of retail properties. Instagram and Twitter, two major LBSN platforms, were selected as social media data sources. First, 17 million geo-tagged Instagram posts and 230 thousand geo-referenced tweets were collected. Then, the data sets were superposed on COVID-19 key points in Turkey and the relationships observed. Finally, the data sets were combined with the commercial real estate data to monitor increases in the accuracy of rent and price predictions. Beşiktaş District of Istanbul was chosen as the pilot region to test the methodology. The results showed that the LBSN-supported models outperformed baseline models most of the time for price predictions and occasionally for rent predictions. Also, both Instagram and Twitter were found essential to the study and could not be omitted. This study demonstrates the significance and leveraging potential of applying human activities to the decision-making processes of the commercial real estate sector under COVID-19 conditions. This is the first study to adopt LBSN data to forecast commercial property prices.

Analyzing the Influence of Metro Stations on Commercial Property Values in Delhi: A Hedonic Approach

Abstract The effect of proximity to a transit system on property values has become a key issue of debate regarding public infrastructure and economic development. This article aims to examine the impact of selected stations along the Blue line of Delhi Mass Rapid Transit System (MRTS) on commercial property prices. The research analyzed 1,413 commercial property parcels sold before and after the commissioning of Blue line in 2005. Hedonic Price Analysis (HPA) was used to estimate the effects of proximity to the metro rail on commercial property values. The method was applied to two time periods, i.e., from 2000-2004 and 2005-2008, coinciding with planning and construction (pre-commissioning phase and the operation phase (post-commissioning phase) of metro rail using actual sale prices of commercial units. The results indicate that a station node shows a negative trend during the planning and construction period. However, the operation period has produced a significant price premium associated with commercial properties, connected with improved accessibility. The coefficients indicate that MRTS has induced an increase in prices from INR 732.80 to INR 246.19, and its radius of impact covers an area of approximately 1/2 km from the stations. The methodology and results provide insight with a specific focus on commercial real estate values in other metropolitan cities developing and expanding MRTSs.

Forecasting US Commercial Property Price Indexes Using Dynamic Factor Models

The general purpose of a dynamic factor model (DFM) is to summarize a large number of time series into a few common factors. In this paper we explore several DFMs on 80 granular, non-overlapping commercial property price indexes in the US, quarterly from 2001Q1 to 2017Q2. We examine the nature and the structure of the factors and the index forecasts that can be produced from the DFMs. We consider specifications of one to four common factors. As a major motivation for the use of DFMs is their ability to improve out-of-sample forecasting of systems of numerous related series, we apply the DFM estimated factors in an Autoregressive Distributed Lag (ARDL) model to forecast individual market index returns. We compare for four markets the forecasts to those from a benchmark univariate autoregression. The results show that the DFM & ARDL model predicts the crisis and subsequent recovery really well, whereas the benchmark model typically extrapolates the past price trend.

서울시 ‘~리단길’ 골목상권 상가거래가격에 영향을 미치는 요인 분석*

‘~Ridan-gil’ is a similarly named commercial streets which has developed into a brand through social media. Though it is one of the benchmarking elements of street revitalization projects, such streets are indeed declining due to gentrification. Accordingly, the purpose of this study is to identify the factors that affect the commercial property price of ‘~Ridan-gil’ side streets in Seoul. As a methodology, this study conducted a multiple regression analysis on the physical and social determinants of commercial property price shared between ‘~Ridan-gil’ side streets in Seoul. As the result, factors with positive correlation include ‘internet search frequency’, ‘the number of bus stops in range’, ‘the number of subway stations in range’, ‘floor area’, ‘floating population in range’, ‘building age’ and ‘main street oriented dummy’. Factors with negative correlation were ‘third-class small road dummy’ and ‘merchant-led dummy’. This study empirically analyzed how the characteristics shared between ‘~Ridan-gil’ side streets affected the commercial property price of the area. As the shared features between ‘~Ridan-gil’ do significantly affect the commercial property price, it is empirically proven that the similarly-named street do act as a brand. However, in terms of accessibility, there is few difference with other types of commercial districts or streets.

A REVIEW OF SPATIAL ECONOMETRICS IN EXPLICIT LOCATION MODELLING OF COMMERCIAL PROPERTY MARKET

‘Location, location, location’ is a real property parlance mostly used to describe the influence of location in the property market. Location is mainly considered as the most significant influencer of commercial property prices. Location is modelled traditionally using hedonic pricing model by either proxy location dummies or distances relative to other neighbourhood features. This was shown to be inadequate due to spatial autocorrelation and heterogeneity inherent in spatial data, which jeopardises the estimates' consistency. Consequently, spatial econometrics is used to explicitly model location into property pricing by controlling spatial effects of autocorrelation and heterogeneity. Housing studies dominate the use of this approach with limited application in the commercial property market. This paper reviewed spatial econometrics and found that the commercial property market exhibits significant spatial dependence and heterogeneity. Accounting for such effects improves model accuracy significantly. It, therefore, recommends increase use of spatial econometrics in commercial property market modelling.

Responses of REITs index and commercial property prices to economic uncertainties: A VAR analysis

We use market participants’ perceived uncertainty to investigate the response of real estate investment trusts index (REITs Index) and commercial property prices to shocks in economic uncertainty. Using US quarterly data and applying a vector autoregression (VAR) model, our results show that an increase in market participants’ perceived uncertainty leads to a significant drop in the REITs Index and commercial property prices. In addition, we show that the REITs Index responds quicker to the uncertainty shocks than the commercial property prices. Our findings provide important implications for investors.

Compilation of Commercial Property Price Indices for Germany Tailored for Policy Use

Abstract The compilation of commercial property price indices (CPPIs) is challenging. Policymakers urge for timely, reliable and comprehensive data. In Germany, lack of data prevents the calculation of official figures by the national statistical authority. Different applications of price indices need different definitions of commercial real estate. CPPIs according to these definitions are constructed on the basis of existing data for 127 German towns and cities (that cover about one-third of German population). The overall price developments revealed by the various indices are rather similar in terms of central time series characteristics, while differences in detail can be explained by their specific compositions. Price increases for all definitions have been strongest in the seven largest cities. The definitions tend to lead to more marked differences for medium-sized towns.

Land Price Dynamics and Macroeconomic Fluctuations with Imperfect Substitution in Real Estate Markets

The collateral channel, whereby an increase in residential house prices leads to an increase in commercial property prices, loosening firm borrowing constraints and increasing firm investment, is weaker when residential and commercial real estate are imperfect substitutes. We enrich the DSGE model of the collateral channel in Liu, Wang, and Zha (2013) by adding a land development sector to allow residential and commercial land to be imperfect substitutes. Bayesian estimation of our structural model based on aggregate U.S. data indicates imperfect substitution between the two types of land. With variance decompositions and impulse responses, we show that the strength of the collateral channel linking residential house prices and firm investment is weaker when the two types of land are imperfect substitutes. When residential and commercial land are assumed to be perfect substitutes, shocks to residential real estate demand explain 20-30% of the variance of total output and 30-40% of the total investment, but with the imperfect degree of substitution that we estimate, the same shocks explain less than 10% of the variance of output and 12-15% of investment.

Leveraged Property Cycles

This paper studies the effects of imperfect risk-sharing between lenders and borrowers on commercial property prices and leverage. The key friction is that agents use different discount rates to evaluate future flows. Eliminating this pecuniary externality generates large reductions in the volatility of real estate prices and credit. Therefore, policies that enhance risk-sharing between lenders and borrowers reduce the magnitude of boom-bust cycles in real estate prices. We also introduce health shocks to study the effect of the COVID-19 crisis on the commercial property market.

Land Price Dynamics and Macroeconomic Fluctuations with Imperfect Substitution in Real Estate Markets

The collateral channel, whereby an increase in residential house prices leads to an increase in commercial property prices, loosening firm borrowing constraints and leading to higher firm investment, is weaker when residential and commercial real estate are imperfect substitutes. We first show in a reduced form regression 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 modify the DSGE model of the collateral channel in Liu, Wang, and Zha (2013) to allow imperfect substitutability between residential and commercial land. With Bayesian estimation and U.S. data, we estimate that the elasticity of substitution between the two types of land is 0.88. Variance decompositions and impulse responses show that the strength of the collateral channel linking house prices and investment is weaker when the two types of land are imperfect substitutes

Determinants of stock return of property and real estate companies in the developing market

Macroeconomic risk factors can determine the expected return on property and real estate stocks (Khan, Khan, Ahmad, & Bashir, 2021), in addition to other factors: property prices (Das, Füss, Hanle, & Russ, 2020) and financial performance (Medyawati & Yunanto, 2017). This study aims to empirically prove the effect of interest rates (SB), exchange rates (KURS), commercial property price index (IHPK), return on assets (ROA), debt-to-equity ratio (DER), and current ratio (CR) on stock returns estimated using panel data regression model. The sample of this research is 23 companies from 63 companies in the property and real estate industry which are listed on the Indonesia Stock Exchange (IDX) during the 2015–2019 period. The empirical findings of this study prove that the ROA, CR, IHPK, and KURS variables have a negative effect on stock returns, while the SB variable has a positive effect. The level of corporate debt (DER) was not proven to determine stock returns. The exchange rate has the greatest influence on stock returns, and the fact does show that the Indonesian stock market is dominated by foreign investors, so that every time foreign currencies appreciate because they leave the stock exchange, the stock price immediately declines. The results of this study have implications for investors that investment decisions to buy shares of property and real estate companies must understand the changes that occur, especially macroeconomic variables and also the company’s financial performance

Impact of rail transit station proximity to commercial property prices: utilizing big data in urban real estate

Urban transport investments have contributed to the exponential increase of value from land and properties around the built infrastructure. Although literature had shown evidence of value uplift from the residential property due to transit development, little is known about the impact on commercial property values. This paper aims to evaluate the impact of rail transit proximity to the commercial property market by taking into account the pre-operation of the LRT project in Jakarta, Indonesia. This study utilizes a big data approach to accelerate research data processing by employing data mining method, as well as geographical information system (GIS) and hedonic price modeling (HPM) to investigate the property prices to formulate empirical evidence for the research. The results show opposite evidence compared to previous studies which argued that accessibility may contribute to the property price when closer to the transit station. The findings indicate that proximity to rail transit has an insignificant impact on the commercial property price compared to other variables such as building size, number of rooms, location, and hospitals. A comparison of the findings between this research with other studies was discussed and recommendation for further research development was proposed.

Commercial Property Price Indices and Indicators: Review and Discussion of Issues Raised in the CPPI Statistical Report of Eurostat (2017)

Commercial property price indices (CPPIs) are needed to monitor financial stability, guide investment decisions by firms, and improve national accounts. Due to a lack of suitable data, however, reliable CPPIs are often hard to construct. Here we survey the current state of the CPPI literature and assess the contribution of the recently published Eurostat CPPI report.

Compilation of Commercial Property Price Indices for Germany Tailored for Policy Use

The compilation of commercial property price indices is a big challenge. In Germany, substantive data gaps prevent the calculation of official figures by the national statistical authority. By contrast, policymakers urge for timely, reliable and comprehensive data. In this paper, proposals are made as to how to aggregate and classify individual price information in order to best serve the intended policy uses. Experimental price indices according to various definitions of commercial real estate are constructed on the basis of two components: (i) the appraisals for transaction prices of houses, apartments, multi-family dwellings, office buildings and retail space in 127 German towns and cities provided by bulwiengesa, a real estate consulting company; and (ii) corresponding data on floor space which make it possible to derive coherent weighting schemes. The overall price developments revealed by the various indices are rather similar in terms of central time series characteristics, while differences in detail can be explained by their specific compositions. Analysts may find these indices helpful to better understand price developments in German commercial real estate markets. Statisticians may acquire from this exercise further knowledge about measurement practices, as official statistics are encouraged to take steps towards establishing thorough reporting on commercial real estate markets.

The impact of sale and leaseback on commercial real estate prices and initial yields in the UK

ABSTRACTThis research evaluates the impact of Sale and Leaseback (SLB) on UK commercial property prices and yields, compared with arms-length transactions. Data on 357 SLB deals and 1266 non-SLB deals are extracted from CoStar and EGI. Hedonic regressions and comparative analysis with the risk-free rate are undertaken. In addition to the SLB dummy variable, explanatory variables include building size, quality, age, sector, location and year of transaction. SLB transactions are found to occur at a statistically significant price premium, with the greatest premium occurring in the office sector. SLB properties achieve a 4.5 percentage point premium compared with the risk-free rate. The net initial yield of the SLB transaction sample is around 2 percentage points lower than for the non-SLB sample in every sector. Reasons for these differences are probed by considering the effect of WAULT and tenant covenant strength. From a vendor’s perspective, the results give an indication of the price premium they might be able to negotiate for their property compared with market prices. This will help them assess whether SLB is worthwhile compared with other available financing options. The findings of a yield reduction should help investors decide whether to engage in SLB investment.

Where Is the Opportunity in Opportunity Zones? Early Indicators of the Opportunity Zone Program’s Impact on Commercial Property Prices

In December 2017, the U.S. Congress passed into law the Opportunity Zone (OZ) pro- gram. As an opportunity zone, designated low-income census tracts provide consider- able tax breaks to local property investors, intending to attract investments and conse- quentially spur economic growth. The success of the OZ program is therefore dependent on investors’ responses, increasing investments or forgoing the opportunity. We analyse commercial property market reactions, comparing price and liquidity in a difference-in- differences framework around the implementation of the OZ program. We identify two effects on property markets: tax breaks for investors and expected land value appreciation due to economic growth. Under an efficient market framework both effects should increase property market values. Our results show that the benefits of tax breaks are priced in effi- ciently. In line with the theoretical benefits, qualified properties increase by 10% – 20% in price, while vacant land prices increase up to 37%. However, we find no signs of expected land value appreciation as nearby properties, unqualified for tax benefits, do not change in value. Overall, our results suggest that investors are aware of the OZ program and poten- tial tax benefits but reserved or anticipating limited future economic growth of OZ census tracts.

Capital Flows, Real Estate, and Local Cycles: Evidence from German Cities, Banks, and Firms

Capital flows and real estate are pro-cyclical, and real estate has a substantial weight in economies' income and wealth. In this paper, we study the role of real estate markets in the transmission of bank flow shocks to output growth across German cities. The empirical analysis relies on a new and unique matched data set at the city level and the bank-firm level. To measure bank flow shocks, we show that changes in sovereign spreads of Southern European countries (the so-called GIPS spread) can predict German cross-border bank flows. To achieve identification by geographic variation, in addition to a traditional supply-side variable, we use a novel instrument that exploits a policy assigning refugee immigrants to municipalities on an exogenous basis. We find that output growth responds more to bank flow shocks in cities that are more exposed to tightness in local real estate markets. We estimate that, during the 2009-2014 period, for every 100-basis point increase in the GIPS spread, the most exposed cities grow 15-25 basis points more than the least exposed ones. Moreover, the differential response of commercial property prices can explain most of this growth differential. When we unpack the transmission mechanism by using matched bank-firm-level data on credit, employment, capital expenditure and TFP, we find that firm real estate collateral as measured by tangible fixed assets plays a critical role. In particular, bank flow shocks increase the credit supply to firms and sectors with more real estate collateral. Higher credit supply then leads firms to hire and invest more, without evidence of capital misallocation.