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The Journal of Real Estate Finance and Economics

Erschienen in:

01.08.2015

Spatial Dependence in International Office Markets

verfasst von: Andrea M. Chegut, Piet M. A. Eichholtz, Paulo J. M. Rodrigues

Erschienen in: The Journal of Real Estate Finance and Economics | Ausgabe 2/2015

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Abstract

This paper investigates spatial dependence in the prices of office buildings in Hong Kong, London, Los Angeles, New York City, Paris, and Tokyo for 2007 to 2013. Compared to prior literature, we find low economic impact from spatial dependence in all six markets, and spatial and spatial-temporal dependence do not moderate the effects of hedonic characteristics statistically or economically. However, investor and seller types as well as neighborhood location have a significant impact on the economic and statistical significance of the spatial and spatial-temporal parameters. Spatial office price indices for London, Paris and Tokyo decline somewhat more than do hedonic indices during the crisis.

Vorheriger Artikel A Market-Specific Methodology for a Commercial Building Energy Performance Index

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See the Real Capital Analytics Ranking tool lists global property markets and players by transaction volume on a rolling 12 month window.

(Fisher et al. 1994) were the first to apply the hedonic framework to commercial real estate. Since that time, there have been approximately 20 studies that study commercial property markets through the hedonic framework. However, most of these studies look at realized rents or asking rents. Just under ten studies look at transaction prices in commercial property and a majority look at property price trends in the US.

Fisher et al. (1994) specify a hedonic model that measures the variation in price per square foot through property type, function, location, quality, local income, population, net income and capital expenditures of the owner.

Colwell et al. (1998) extended the hedonic model for Chicago office property by including more building characteristics, e.g., age, lot area, size and height, and more neighborhood characteristics, e.g. distances to airport, rail and road facilities as well as golf-courses to explain the variation in the log price per square foot.

There are other neighborhood and time components included in the study: metropolitan area dummies and property type dummies.

Tu et al. (2004) includes the floor area of the office unit, the age of the unit, the floor level where the office unit is located and whether the unit was leasehold to explain the variation in transaction prices. Nappi-Choulet and Maury (2009) document for Parisian sub-markets size, age and period of construction to explain variation in prices.

Chegut et al. (2014) broaden their controls to include transportation networks, investor types and per unit building quality characteristics to explain the price per square foot.

The hedonic literature is inconsistent in its usage of log price or price per square foot. However, given the heterogeneous size of buildings within our sample and the cross sample differences in building customs, we follow (Fisher et al. 1994) and (Fisher et al. 2007) who estimate models using price per square foot to control for cross-market building size heterogeneity. Moreover, size is correlated with other hedonic factors, e.g., age or stories, and without filtering for size reflects a size and hedonic attribute component.

(W ⊙ L) specifies the weight by using both the space and time of the nearest neighbor. It also ensures that each observation relates to its own nearest neighbor observation and not the neighbors of other buildings.

Data on Hong Kong’s office market reflects office condos as opposed to full building transactions.

Within the model we specify a g e and a g e ² as these explain the depreciation component of the variation in transaction prices. Bokhari and Geltner (2014) document for US markets the impact of depreciation on commercial property values over the 2001 to 2013 period. The proportion of prices explained by depreciation varies across markets, but is economically significant across US markets.

The calculation for the average spatial dependence effect estimated by Eq. 3 is specified as follows: when calculated at the mean $\rho [\sum _{i = 1}^{J=N}W_{ij}]logP_{j}$ this yields $1.39 for the London office market, ceteris paribus.

(Komatsu and Shimazu 2003) document high levels of depreciation for commercial property in Tokyo.

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Titel: Spatial Dependence in International Office Markets
verfasst von: Andrea M. Chegut
Piet M. A. Eichholtz
Paulo J. M. Rodrigues
Publikationsdatum: 01.08.2015
Verlag: Springer US
Erschienen in: The Journal of Real Estate Finance and Economics / Ausgabe 2/2015
Print ISSN: 0895-5638
Elektronische ISSN: 1573-045X
DOI: https://doi.org/10.1007/s11146-014-9484-x

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