Price and Transaction Volume in the Dutch Housing Market

Wit, Erik R. de; Englund, Peter; Francke, Marc

doi:10.2139/ssrn.1589296

Cited by 6 publications

(8 citation statements)

References 36 publications

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“…Market liquidity is defined as the ease at which assets can be traded, while funding liquidity refers to the ease with which they can be financed. In the housing market literature, examples of market liquidity include the rate of sale (Hort 2000, Genesove and Mayer 2001, De Wit, Englund and Francke 2013, (seller) time on market (Kang and Gardner 1989, Jud, Seaks and Winkler 1996, Glower, Haurin and Hendershott 1998 and the number of transactions (Wu and Brynjolfsson 2014). An example of funding liquidity is the ease with which a mortgage can be obtained (i.e., credit constraints, see Mian and Sufi 2009, Duca, Muellbauer and Murphy 2011, Francke, Van de Minne and Verbruggen 2014.…”

Section: Market Liquiditymentioning

confidence: 99%

“…Following Genesove and Mayer (2001), De Wit, Englund and Francke (2013), among others, we employ the rate of sale (i.e., sales in a given period divided by the number of houses for sale at the beginning of the period) as a market liquidity measure. We denote this variable the rate of sale or ros in short.…”

Section: Introductionmentioning

confidence: 99%

“…Our study is related to literature that examines the relationship between prices and liquidity. Booming housing markets are typically characterized by more liquidity, while bust markets with declining prices usually show less liquidity (Stein 1995, Clayton, Miller and Peng 2010, De Wit, Englund and Francke 2013. A large part of the literature that examines the relationship between house prices and market liquidity uses shocks in, for example, the labor market (Clayton, Miller and Peng 2010) or the mortgage rate (Hort 2000, De Wit, Englund and to determine the price-volume correlation.…”

Section: Introductionmentioning

confidence: 99%

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Internet Search Behavior, Liquidity and Prices in the Housing Market

Dijk

Francke

2017

Real Estate Economics

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We employ detailed internet search data to examine price and liquidity dynamics of the Dutch housing market. We show that the number of clicks on properties listed online proxies demand and the number of listed properties proxies supply. From this internet search behavior, we create a market tightness indicator and we find that this indicator Granger causes changes in both house prices and housing market liquidity. The results of a panel VAR suggest that a demand shock results in a temporary increase in liquidity and a permanent increase in prices in urban areas. This is in accordance with search and matching models.

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Section: Market Liquiditymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Internet Search Behavior, Liquidity and Prices in the Housing Market

Dijk

Francke

2017

Real Estate Economics

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“…As a result the structural break in the market is 'ignored', as it happens. This may be of particular concern for a sudden downturn in the market, as the number of observations will drop considerably as well see Fisher et al (2003) andDe Wit et al (2013) for a detailed explanation on the positive price-volume correlation found in real estate.…”

Section: Resultsmentioning

confidence: 99%

Using Revisions as a Measure of Price Index Quality in Repeat-Sales Models

Minne

Francke

Geltner

et al. 2019

J Real Estate Finan Econ

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Repeat-sales indexes are the most widely used type of transaction based property price indexes. However, such indexes are particularly prone to revision. When a new period of transaction data becomes available and is used to update the repeat-sales model, all past index values can potentially be revised. These revisions are especially problematical for commercial real estate (as compared to housing), due to the relative scarcity of transaction data and the heterogeneity of the underlying properties. From a methodological perspective, the magnitude of the revisions is a particularly useful measure of the index quality, as it directly reflects both the precision of the index and its practical usefulness in economic and business applications. This paper focuses on index revisions in thin, commercial property markets, the type of market that is most challenging. We present multiple specifications of the repeat-sales model (both existing and new), seeking to reduce revisions. We are able to reduce overall index revisions by more than 50%, compared to more traditional repeat-sales models.

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“…In the appendix we also provide the distributions over year of sale (Table 9), location (Table 10) and investor type (Table 11). Given the often found positive price-volume correlation in real estate (van Dijk et al 2019;De Wit et al 2013), it is not surprising to find that the smallest percentage of transactions was during the GFC, in 2008-2010 (Table 9). Transaction volume is now higher compared to the previous, pre-GFC peak in 2007.…”

Section: Data and Descriptive Statistics Of The Other Variablesmentioning

confidence: 93%

Preferences of Institutional Investors in Commercial Real Estate

Cvijanović

Milcheva

Minne

2021

J Real Estate Finan Econ

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In this paper we analyze market segmentation by firm size in the commercial real estate transaction process. Using novel micro-level data, we look at the probability distribution of investors acquiring a specific bundle of real estate characteristics, distinguishing between investors based on the size of their real estate portfolio. We find evidence of market segmentation by investor size: institutional investors segment across property characteristics based on the size of their real estate portfolio. The probability that a large (small) seller will sell a property to a similar-sized buyer is higher, keeping all else equal. We explore potential drivers of this market segmentation and find that it is mainly driven by investor preferences. During the Global Financial Crisis (GFC), large investors were less likely to buy the ‘average’ property, as compared to the period before or after the crisis, indicating time-varying investor preferences.

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Price and Transaction Volume in the Dutch Housing Market

Cited by 6 publications

References 36 publications

Internet Search Behavior, Liquidity and Prices in the Housing Market

Internet Search Behavior, Liquidity and Prices in the Housing Market

Using Revisions as a Measure of Price Index Quality in Repeat-Sales Models

Preferences of Institutional Investors in Commercial Real Estate

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