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Clientele Effects on the Demand For Housing Price Appreciation

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  • David Dale-Johnson

Abstract

If house buyers are segmented by income, one might expect to observe buyers in such markets valuing the benefits of expected capital gain differently. Presumably, individuals experiencing higher marginal tax rates should be inclined to pay relatively more for anticipated capital gain since the opportunities of sheltering such income from taxation are greater. This paper attempts to identify a proxy for expected capital gain by using the hedonic price methodology to predict a recent price series for each housing unit in a sample of sales. That proxy is then used to determine an individual buyer's marginal willingness to pay for anticipated price appreciation. The results indicate that one cannot reject the joint hypothesis that homebuyers naively extrapolate from prior implied price performance to establish future price expectations and the variation in willingness to pay for that expectation may be a function of the buyer's income. This suggests the existence in housing markets of a phenomenon termed the clientele effect. This effect has been the subject of considerable examination in the finance literature. Copyright American Real Estate and Urban Economics Association.

Suggested Citation

  • David Dale-Johnson, 1983. "Clientele Effects on the Demand For Housing Price Appreciation," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 11(3), pages 382-396.
  • Handle: RePEc:bla:reesec:v:11:y:1983:i:3:p:382-396
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    Cited by:

    1. Terrence Clauretie & Paul Thistle, 2007. "The Effect of Time-on-Market and Location on Search Costs and Anchoring: The Case of Single-Family Properties," The Journal of Real Estate Finance and Economics, Springer, vol. 35(2), pages 181-196, August.
    2. Nils Soguel & Marc-Jean Martin & Alexandre Tangerini, 2008. "The Impact of Housing Market Segmentation between Tourists and Residents on the Hedonic Price for Landscape Quality," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 144(IV), pages 655-678, December.

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