1,720,989 research outputs found
Real Estate Price Indices and Price Dynamics: An Overview from an Investments Perspective
No full textThis article reviews the state of the art in real estate price indexing and the state of knowledge about real estate price dynamics, with a focus on investment property, or income-generating commercial property. Investment properties form a large component of the national wealth and of capital markets and represent a major investment asset class. They are characterized by various types of heterogeneity, including among assets, markets, and data sources, making the study of real estate pricing uniquely challenging. Yet in recent decades, urban economists and econometricians have pioneered major new price indexing methodologies that, combined with new types of data sources, are shedding light on the nature of commercial property price dynamics, revealing both important commonalities and unique differences compared with equities and fixed-income securities pricing. Keywords: commercial property; real estate; price indexing; price dynamics; asset market
The Housing Market Effects of Local Home Purchase Restrictions: Evidence from Beijing
No full textHome prices have surged in major Chinese cities, leading to concerns of asset price bubbles and housing affordability. The policy of home purchase restrictions (HPR) has been one of China’s harshest housing market interventions to squeeze out speculative demand and dampen the soaring home prices. Beijing was the first city to implement the HPR. Employing the regression discontinuity design technique, we find that Beijing’s HPR policy triggered a 17–24 % decrease in resale price, a drop in the price-to-rent ratio of about a quarter of its mean value, and a deep (1/2 to 3/4) reduction in the transaction volume of the for-sale market, with no significant change in the rent or the transaction volume of rental units. In submarkets where housing supply was less elastic, the effects of the HPR were larger in price and smaller in quantity, suggesting that wealthy buyers likely benefited more from the HPR. The scope of the analysis does not allow conclusions regarding the persistence or longevity of these effects
Do Different Price Points Exhibit Different Investment Risk and Return in Commercial Real Estate?
No full textConventional real estate price indexes provide a single measure for the path of asset prices over time (controlling for the quality of the representative or average property). Properties could, however, have different price dynamics based on the price segment in which they are traded. On the demand side, investors at different price points are differentiated by the amount of capital they have at their disposal and the type and source of financing. Smaller, private investors cluster at lower price points, whereas large institutions dominate the high price points. On the supply side, properties at different price points may serve different space markets with different types of tenants and may reflect different supply elasticity and land/structure value ratios. In this article, the authors use an unconventional approach, quantile regression, to estimate price indexes for different price segments in commercial real estate. Their results show that there are indeed large differences in price dynamics for different price points. These differences are suggestive of a lack of integration in the property asset market because the authors find apparent differences in the risk–return relationship. Lower-price-point properties exhibit less risk (in the form of volatility and cycle amplitude) but have no evidence of lower total returns. Lower-price-point properties also show greater momentum and thus greater predictability. Keywords: commercial real estate; quantile regression; chained hedonic index; investment property; equilibrium asset pricing; price of ris
A new approach for constructing home price indices: The pseudo repeat sales model and its application in China
No full textThis paper develops a “pseudo repeat sale” estimation sample construction procedure (ps-RS) to construct more reliable and less biased quality-controlled price indices for newly-constructed homes. The method may be useful wherever new housing development is of sufficiently large scale and homogeneous. Such circumstances characterize many emerging market countries, and here we apply the technique in China. We match two very similar new sales within a defined matching space. Here we test three versions of matching spaces – complex, phase, and building. We then regress the within-pair price differentials onto time dummies and the differentials in unit-specific physical attributes. Locational and community variations, as well as many unobservable or difficult to measure physical attribute variations, are cancelled out in the model, and thereby controlled for. The building-version ps-RS index does the best job in this regard because its within-pair differential is the smallest. We further introduce a “hedonic value” distance metric criterion so that one can deal flexibly with the trade-off between the within-pair “similarity” and the sample size. We explicate and demonstrate formal signal-to-noise oriented metrics of index quality, which can be superior to traditional standard errors based metrics, and we use the new metrics to compare index construction methodologies. The ps-RS approach addresses the problem of lack of repeat-sales data in emerging markets and newly constructed properties and the omitted variables problem in the hedonic method. It also addresses the traditional problems with the classical same-property repeat-sales model in terms of small sample sizes and sample selection bias.
The present paper tests the ps-RS method using a large-scale micro transaction data set of new home sales from January 2006 to June 2011 (444,596 observations) in Chengdu, Sichuan Province, China. The resulting complex-based ps-RS index essentially parallels the hedonic index, suggesting that the hedonic index is not superior to that version of the ps-RS index in terms of systematic results. The phase-based ps-RS index has a lower growth trend and the building-based version lower still, indicating omitted variables relating to the physical quality of the units are not well controlled for in the hedonic, and suggesting that the building-based version of the ps-RS index provides the greatest control for such quality differences. Building-based ps-RS indices with different distance metric thresholds are almost the same. Compared to the hedonic, the ps-RS provides a smoother index indicating less random estimation error (or “noise”)
Uncertainty, Flexibility, Valuation and Design: How 21st Century Information and Knowledge Can Improve 21st Century Urban Development
No full textThe 21st century presents humankind with perhaps its greatest challenge since our species almost went extinct some 70,000 years ago in Africa. A big part of meeting that challenge lies in how the urbanization of three billion additional people (equal to the entire world population in 1960) will be accomplished between now and mid-century, on top of necessary renewal and renovation of the earth’s existing cities. China alone will urbanize 300 million more people between now and 2030, equal to the entire population of the U.S., the world’s third most populous country, in just 20 years. This is development on a scale and pace that is an order of magnitude greater than the past century, in a world resource and climate environment that is near the breaking point, in a context of greater technological, financial, and economic uncertainty than ever before.
To meet this challenge will require that we use the best tools in our kit, including ones that have become available to us only in this new knowledge and information-based century. Technology got us here, and technology will be key to getting us through. In this paper we will review and synthesize two important methodological developments in our profession that can help infrastructure and real estate physical development (i.e., urban development) to be accomplished more effectively and efficiently in a world of uncertainty.
The first methodological development is the honing of real options theory and methodology for practical application to identify and evaluate sources of flexibility in the design and operation of capital projects. The second development is the marriage of digital data compilation of property transactions records with the honing of econometric analysis methodology to allow the practical quantification of real estate and infrastructure asset price dynamics.
We argue that this latter development provides the key input to the former development, enabling a much more complete and rigorous treatment of design and evaluation problems for urban development. We also argue that an engineering systems approach to option modelling is likely to find better traction in actual professional practice than the economic theoretical models that have dominated the academic literature. We provide a concrete example by applying the suggested approach to the Songdo New City development in Korea.
The result can be better informed design and valuation and more efficient urban development laced with greater flexibility to avoid the worst down-side outcomes and to take advantage of the best up-side opportunities, saving vital resources of capital, land, raw materials, and energy
2013 Maastricht-NUS-MIT International Real Estate Finance & Economics Symposium: Editors’ Introduction to the Special Issue
No full textThis article provides the Editors’ Introduction to this special issue, which presents articles first presented at the 2013 Maastricht-NUS-MIT (MNM) Symposium on International Real Estate Finance and Economics, held at MIT in October 2013. This Introduction briefly describes each of the eight articles in the special issue, which are grouped into three broad topic areas: REITs, Real estate debt behavior, and Urban economics
Erratum to: Introduction to the Special Issue
Get PDFThe papers, “Model Stability and the Subprime Mortgage Crisis,” by X. An, Y. Deng, E. Rosenblatt and V.W. Yao and “Pricing Inefficiencies in Private Real Estate Markets Using Total Return Swaps,” by C. Lizieri, G. Marcato, P. Ogden and A. Baum were scheduled to be part of the special issue. Instead, the papers were published in Volume 45, Number 3, 2012, pages 545 and 774, respectively
The 2014 Maastricht-NUS-MIT International Real Estate Finance & Economics Symposium: Editors’ Introduction to the Special Issue
Get PDFThis is the Editors’ Introduction to the special issue of the Journal of Real Estate Finance and Economics. The issue includes nine papers presented at the 2014 Maastricht-NUS-MIT (MNM) Symposium on International Real Estate Finance and Economics, held at Maastricht University in September 2014. This Introduction briefly describes the articles included in the special issue. The papers cover a broad range of topics
Estimating Real Estate Price Movements for High Frequency Tradable Indexes in a Scarce Data Environment
No full textIndexes of commercial property prices face much scarcer transactions data
than housing indexes, yet the advent of tradable derivatives on commercial property
places a premium on both high frequency and accuracy of such indexes. The
dilemma is that with scarce data a low-frequency return index (such as annual) is
necessary to accumulate enough sales data in each period. This paper presents an
approach to address this problem using a two-stage frequency conversion procedure,
by first estimating lower-frequency indexes staggered in time, and then applying a
generalized inverse estimator to convert from lower to higher frequency return
series. The two-stage procedure can improve the accuracy of high-frequency indexes
in scarce data environments. In this paper the method is demonstrated and analyzed
by application to empirical commercial property repeat-sales data.Real Capital Analytics (Firm)Real Estate Analysts Limite
Loss aversion and anchoring in commercial real estate pricing: Empirical evidence and price index implications
Get PDFLoss aversion behavior plays a major role in the pricing of commercial properties, and it varies both across the type of market participants and across the cycle. We find that sophisticated and more experienced investors are at least as loss averse as their counterparts and that loss aversion operated most strongly during the cycle peak in 2007. We also document a possible anchoring effect of the asking price in influencing buyer valuation and subsequent transaction price. We demonstrate the importance of behavioral phenomena in constructing hedonic price indices, and we find that the impact of loss aversion is attenuated at the aggregate market level. This suggests that the pricing and volume cycle during 2001–2009 was little affected by loss aversion.Real Capital Analytics (Firm)Real Estate Research Institut
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