Commercial Real Estate Returns Research Articles

Market perceptions of distress and its impact on returns: evidence from the U.S. commercial real estate market

ABSTRACT This paper examines the relationship between market participants’ sentiment regarding distress in the real estate market, captured through internet search queries, and the U.S. commercial real estate market. We use Google search frequency to construct a measure of market perception of distress, the Market Distress Perception Index (MDP Index), and determine the extent to which this index can help predict future private commercial real estate returns in the U.S. The results show that our pessimistic sentiment index predicts returns of commercial real estate up to three quarters in advance. These results hold at the national, regional, and state levels and are robust across various model specifications. The predictability of the sentiment index is especially pronounced during recessionary periods. We also analyse different property sectors and find that the inverse relationship between the MDP Index and future private commercial real estate returns persists across all sectors except the industrial sector. The relationship is strongest for the office sector.

Commercial Real Estate Returns and Innovation

We investigate the relation between commercial real estate (CRE) returns and regional innovativeness and find that regions with more innovation exhibit higher total returns on commercial property. And, when we investigate the extent to which income return and capital return on commercial properties are related to local innovativeness, we report a positive relation between innovativeness and income return but little to no association between innovativeness and capital return. Our paper also provides some initial evidence suggesting that local innovativeness is related to better future CRE performance. Overall, this paper suggests that the innovativeness of a region is economically and statistically positively related to CRE returns, adding to a budding literature investigating CRE returns and extensive literature researching innovation.

Heat Stress and Commercial Real Estate Returns

Heat Stress and Commercial Real Estate Returns

Can the Textual Tone in REIT Financial Statements Improve the Information Environment for Commercial Real Estate Investors? An Investigation

The SEC requires publicly traded REITs to discuss commercial real estate market conditions in their financial statements in order to provide REIT investors with decision-relevant information. We investigate whether the textual tone in REIT financial statements (10-K/10-Q) can be used to predict future commercial real estate returns. In our empirical investigation, we focus on forward-looking tone, i.e., tone that cannot be explained by a REIT’s past performance. We extract textual tone from quarterly REIT financial statements over the period from 2002 to 2017 by using both a generic finance dictionary and a new, REIT-specific dictionary. We then employ a regime-switching methodology to assess the informative value of forward-looking tone for commercial real estate returns in the next quarter. We find that in periods of poorly performing real estate markets the forward-looking tone in REIT financial statements predicts total returns in the next quarter. This effect is driven by capital returns and is most pronounced for apartment and retail. Our findings suggest that the tone in REIT financial statements can help improve the information environment for commercial real estate investors.

The effect of sentiment on commercial real estate returns: investor and occupier perspectives

PurposeReal estate is an asset that is traded in highly segmented, illiquid and informationally inefficient local markets. A short sale in real estate is almost infeasible and therefore impedes informed rational arbitrageurs to trade against mispricing. Thus, real estate returns are prone to sentiment-driven behaviours. Will the impacts on asset returns be identical for different types of sentiment?Design/methodology/approachThis study argues that not all sentiment effects are created equal. Using the bounds test of the autoregressive distributed lag (ARDL) models, this paper examines how occupier sentiment versus investor sentiment contributes to the short-run and long-run dynamics of commercial real estate returns in Australia.FindingsThe empirical evidence suggests that investor sentiment and occupier sentiment influence return asymmetrically after macroeconomic conditions are controlled for.Practical implicationsThe sectoral analysis further reveals that sector-specific sentiment plays a significant role in explaining commercial real estate returns. Furthermore, notable improvement is found in producing more accurate prediction in returns, given that measures of occupier and investor sentiment are appropriately specified in the forecast.Originality/valueThis study is novel in the sense that it acknowledges the impacts of occupiers' and investors' sentiment may be fundamentally different. The unique innovation and contribution of this study to behavioural finance literature are based on a new dataset from the Royal Institute of Chartered Surveyors which includes a survey-based measure of investor sentiment and occupier sentiment.

Private-equity commercial real estate, timberland, and farmland: market integration and information transition dynamics

Using data from the National Council of Real Estate Investment Fiduciaries (NCREIF), we examine market integration of commercial real estate and timberland–farmland assets via the Fama–MacBeth two-step approach under the intertemporal capital asset pricing framework. In addition, we study the information transition dynamics between those markets via the vector error correction model (VECM). We find evidence of market segregation and one-way information flow from the timberland–farmland market to the commercial real estate market. We conclude that commercial real estate and timberland–farmland assets are driven by different market fundamentals and that lagged timberland–farmland returns can help predict current commercial real estate returns. The only exception is during market downturns when commercial real estate and timberland–farmland markets are somewhat integrated and driven by some factors that are not specified in this study.

Understanding the relationship between public and private commercial real estate markets

ABSTRACT This paper provides a modelling framework to examine the very low correlation at short horizons and high correlation at long horizons between private and public commercial real estate returns. For this purpose, we use a correlated, unobserved component model with a common trend and Markov-switching heteroskedasticity. This model decomposes the public and private commercial real estate prices into a common trend and interdependent cycles. The proposed model is able to endogenously capture low and high volatility regimes in real estate markets. More importantly, our model shows that the low correlation observed at short horizons between the public and private real estate markets is mainly due to the absence of any correlation in low-volatility regimes. On the other hand, the cycles, or short-run movements, in these two markets are highly correlated in high-volatility regimes.

Asset Location, Timing Ability and the Cross‐Section of Commercial Real Estate Returns

AbstractThis study examines the sensitivity of equity REIT returns to time‐varying MSA allocations of REIT property portfolios. Using a large sample of individual commercial property holdings, we find significant cross‐sectional and time variation in REIT geographic exposures and the ability of these exposures to explain the cross‐section of REIT returns. We further find evidence consistent with REIT managers being able, on average, to time allocation decisions ahead of MSA outperformance. This effect is most prevalent in non‐gateway markets, varies significantly across MSAs and over time, and is concentrated in financially flexible firms with a more diversified geographic portfolio.

There's No Place Like Home: Local Asset Concentration, Information Asymmetries and Commercial Real Estate Returns

We provide robust evidence showing local information plays a significant role in local asset concentrations and return outperformance. Using a unique setting with significant cross-market information asymmetries and large sample of individual commercial property holdings, we find property portfolio managers concentrate an economically significant portion of their portfolios in their headquarter location. We further document a significant positive relation between local concentration and portfolio returns in markets where information asymmetry is most severe. Through numerous robustness and loan-level identification tests, we further confirm an information-based channel of asset concentration and return effects that is distinct from risk-based or behavioral explanations.

Asset Location, Timing Ability, and the Cross-Section of Commercial Real Estate Returns

This study examines the sensitivity of equity REIT returns to the time-varying MSA allocations of REIT underlying property portfolios. Using a large sample of individual commercial property holdings, we find significant cross-sectional and time variation in REIT geographic exposures and the ability of these exposures to explain the cross-section of REIT returns. Importantly, the pattern of MSA exposure effects changes quickly as local market information is incorporated into property values both across MSAs and over time. We further find that REIT managers are able, on average, to both identify MSAs that will outperform in the following year and overcome the costs and delays associated with increasing allocations to these MSAs. The ability to time entry into high performing markets is concentrated in well diversified REITs and those with large market capitalizations and high employee counts. Firms with a larger platform and experience owning and operating properties in multiple markets are better positioned to quickly act on investment opportunities they identify in major MSAs.

How Accurate Are Commercial Real Estate Appraisals? Evidence from 25 Years of NCREIF Sales Data

In this study, Cannon and Cole provide new evidence on the performance measurement and reporting of commercial real estate returns. They do so by examining the accuracy of commercial real estate appraisals that occurred prior to the sale of properties from the NCREIF National Property Index (NPI) from 1984 to 2010, a period that spans two up-and-down market cycles. The authors find that, on average, appraisals are more than 12% above, or below, subsequent sales prices that take place two quarters following the appraisal. Even in a portfolio context, allowing for offsetting positive and negative differences, appraisals are off by an average 4%–5% of value, even after adjusting for capital appreciation during those two quarters. The authors also provide new evidence regarding how, and by how much, appraised values lag behind sales prices. They find that appraisals appear to lag the true sales prices, falling significantly below in hot markets and remaining significantly above in cold markets. This new evidence provides guidance to investors, regulators, and others about how to interpret real estate indices such as the NPI, which are based on appraised values, in both a rising and a falling market. Finally, the authors find that this “appraisal error” is largely systematic; they can explain more than half of the variation in the signed percentage difference in sales price and appraised value. Hence, appraisal errors are not due solely to property-specific heterogeneity. <b>TOPICS:</b>Real estate, big data/machine learning, equity portfolio management

How Accurate Are Commercial Real Estate Appraisals?Evidence from 25 Years of NCREIF Sales Data

In this study, we provide new evidence on the performance measurement and reporting of commercial real estate returns. We do so by examining the accuracy of commercial-real-estate appraisals that occurred prior to the sale of properties from the NCREIF National Property Index (“NPI”) during 1984 – 2010, a period which spans two up-and-down cycles of the market. We find that, on average, appraisals are more than 12% above, or below, subsequent sales prices that take place two quarters following the appraisal. Even in a portfolio context, allowing for offsetting positive and negative differences, appraisals are off by an average of 4% – 5 % of value, even after adjusting for capital appreciation during those two quarters. We also provide new evidence regarding how, and by how much, appraised values lag behind sales prices. We find that appraisals appear to lag the true sales prices, falling significantly below in hot markets and remaining significantly above in cold markets. This new evidence provides guidance to investors, regulators and others about how to interpret real-estate indices like the NPI that are based upon appraised values, in both a rising and falling market. Finally, we find that this “appraisal error” is largely systematic; we can explain more than half of the variation in the signed percentage difference in sales price and appraised value. Hence, appraisal errors are not due solely to property-specific heterogeneity.

How Accurate are Commercial Real Estate Appraisals? Evidence from 25 Years of NCREIF Sales Data

In this study, we provide new evidence on the performance measurement and reporting of commercial real estate returns. We do so by examining the accuracy of commercial real-estate appraisals that occurred prior to the sale of properties from the NCREIF National Property Index (“NPI”) during 1984 – 2010, a period which spans two up-and-down cycles of the market. We find that, on average, appraisals are more than 12% above, or below, subsequent sales prices that take place two quarters following the appraisal. Even in a portfolio context, allowing for offsetting positive and negative differences, appraisals are off by an average of 4% – 5 % of value, even after adjusting for capital appreciation during those two quarters. We also provide new evidence regarding how, and by how much, appraised values lag behind sales prices. We find that appraisals appear to lag the true sales prices, falling significantly below in hot markets and remaining significantly above in cold markets. This new evidence provides guidance to investors, regulators and others about how to interpret real-estate indices like the NPI that are based upon appraised values, in both a rising and falling market. Finally, we find that this “appraisal error” is largely systematic; we can explain more than half of the variation in the signed percentage difference in sales price and appraised value. Hence, appraisal errors are not due solely to property-specific heterogeneity.

From Property Companies to Real Estate Investment Trusts: The Impact of Economic and Property Factors in the UK Commercial Property Returns

A great deal of research has examined comovements between commercial real estate returns and macroeconomic variables in the US economy. These relationships have attracted less research interest for the UK real estate market, despite this being the largest European Market. This cross-sectional and time-series study targets this gap in the literature by investigating the impact of economic and property factors – e.g. the UK IPD all property returns, the FTSE all share index returns, and the term structure of interest rates – on commercial property returns. It examines a sample of UK property companies which turned into real estate investment trusts (REITS) following the introduction of the UK legislation permitting this from January 2007. By applying structural time-series modelling and the Kalman Filter to obtain unexpected changes or innovations in selected economic and property variables it was found that economic and property variables influence commercial property returns in the UK. It was also found that by converting property companies into REITS their returns quickly acquired common features of both equities and commercial property backed assets.

Evaluating Unsmoothing Procedures for Appraisal Data

In this paper we investigate alternative methods of adjusting appraisalbased real estate returns to correct for the perceived biases induced in the appraisal process. Since the early work by Geltner (1989), many papers have been written on this topic but remarkably few have provided a detailed statistical evaluation of the proposed unsmoothing methods. To provide a comparison we apply bootstrapping procedures to a sample of individual-property level appraisal data from the Investment Property Database (IPD) to construct aggregate property return series. Applying commonly used unsmoothing methods to these series enables us to draw valuable conclusions about the effectiveness of such procedures in recovering information about the moments of commercial real estate returns.

The Cross‐Sectional Dispersion of Commercial Real Estate Returns and Rent Growth: Time Variation and Economic Fluctuations

We estimate the cross‐sectional dispersions of returns and growth in rents for commercial real estate using data on U.S. metropolitan areas over the sample period 1986 to 2002. The cross‐sectional dispersion of returns is a measure of the risk faced by commercial real estate investors. We document that, for apartments, offices, industrial and retail properties, the cross‐sectional dispersions are time varying. Interestingly, their time‐series fluctuations can be explained by macroeconomic variables such as the term and credit spreads, inflation and the short rate of interest. The cross‐sectional dispersions also exhibit an asymmetrically larger response to negative economics shocks, which may be attributable to credit channel effects impacting the availability of external debt financing to commercial real estate investments. Finally, we find a statistically reliable positive relation between commercial real estate returns and their cross‐sectional dispersion, suggesting that idiosyncratic fluctuations are priced in the commercial real estate market.

Private Commercial Real Estate Equity Returns and Inflation

The authors estimate, for total real estate and each of four property types, the extent to which various private commercial real estate returns, namely income, capital and total return, hedge against expected and unexpected inflation during the 1978-2006 period. Overall, total returns for all of the property types, except for retail, are shown to be a strong hedge against both expected and unexpected inflation. However, and interestingly, this hedging efficacy comes mostly from capital appreciation rather than income return. Thus the inflation hedging power of real estate seems to be more a product of the capital markets, rather than of the net income generating processes of real estate9s leasing and expense structures. In terms of inflation hedging capability, office is shown to be the strongest, followed by apartment and warehouse. Contrary to past studies and conventional wisdom, the retail sector is shown to have no significant hedging power against inflation. <b>TOPICS:</b>Real estate, risk management, statistical methods

The Inflation Hedging Characteristics of US and UK Investments: A Multi-Factor Error Correction Approach

Historic analysis of the inflation hedging properties of stocks has produced anomalous results, with stocks often appearing to offer a perverse hedge. This has been attributed to the impact of real and monetary shocks to the economy, which influence both inflation and asset returns. It has been argued that real estate should provide a better hedge: however, empirical results have been mixed. This paper explores the relationship between commercial real estate returns and economic, fiscal and monetary factors and inflation for US and UK markets. Comparative analysis of general equity and small capitalization stock returns is carried out with inflation divided into expected and unexpected components. The analyses are undertaken using an error correction approach. In the long run, once real and monetary variables are included, asset returns are positively linked to anticipated inflation but not to inflation shocks. Adjustment processes are, however, gradual and not within period. Real estate returns, particularly private market returns, exhibit characteristics that differ from those of stocks.

Valuation in US Commercial Real Estate

Abstract We consider a log‐linearized version of a discounted rents model to price commercial real estate as an alternative to traditional hedonic models. First, we verify a key implication of the model, namely, that cap rates forecast commercial real estate returns. We do this using two different methodologies: time series regressions of 21 US metropolitan areas and mixed data sampling (MIDAS) regressions with aggregate REIT returns. Both approaches confirm that the cap rate is related to fluctuations in future returns. We also investigate the provenance of the predictability. Based on the model, we decompose fluctuations in the cap rate into three parts: (i) local state variables (demographic and local economic variables); (ii) growth in rents; and (iii) an orthogonal part. About 30% of the fluctuation in the cap rate is explained by the local state variables and the growth in rents. We use the cap rate decomposition into our predictive regression and find a positive relation between fluctuations in economic conditions and future returns. However, a larger and significant part of the cap rate predictability is due to the orthogonal part, which is unrelated to fundamentals. This implies that economic conditions, which are also used in hedonic pricing of real estate, cannot fully account for future movements in returns. We conclude that commercial real estate prices are better modelled as financial assets and that the discounted rent model might be more suitable than traditional hedonic models, at least at an aggregate level.

Valuation in the US Commercial Real Estate

We consider a log-linearized version of a discounted rents model to price commercial real estate as an alternative to traditional hedonic models. First, we verify a key implication of the model, namely, that cap rates forecast commercial real estate returns. We do this using two different methodologies: time series regressions of 21 US metropolitan areas and mixed data sampling (MIDAS) regressions with aggregate REITs returns. Both approaches confirm that the cap rate is related to fluctuations in future returns. We also investigate the provenance of the predictability. Based on the model, we decompose fluctuations in the cap rate into three parts: (i) local state variables (demographic and local economic variables); (ii) growth in rents; and (iii) an orthogonal part. About 30% of the fluctuation in the cap rate is explained by the local state variables and the growth in rents. We use the cap rate decomposition into our predictive regression and find a positive relation between fluctuations in economic conditions and future returns. However, a larger and significant part of the cap rate predictability is due the orthogonal part, which is unrelated to fundamentals. This implies that economic conditions, which are also used in hedonic pricing of real estate, cannot fully account for future movements in returns. We conclude that commercial real estate prices, at least at an aggregate level, are better modeled as financial assets and that the discounted rent model might be more suitable than traditional hedonic models, at least at an aggregate level.