In search markets, greater spatial concentration of sellers increases price competition. At the same time, though, a greater concentration of sellers can create a shopping externality by attracting more buyers to the site. Using housing sales data, we test for spatial competition and shopping externality effects on prices and marketing time. We find that they reflect both competitive and shopping externality effects from surrounding houses, although the relative strength varies with how fresh the house is in the market, the freshness of surrounding houses, and the phase of the market cycle. New listings have the strongest shopping externality effect on neighboring houses that have been on the market for some time. Vacant houses have their strongest competition effects in the declining market and externality effects in the rising market. Fresh houses on the market reap little benefit from shopping externalities in all phases of the market cycle. Copyright Springer Science + Business Media, LLC 2006Spatial competition, Shopping externalities, Housing, D83, R21, R31,

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This study examines how individual agents affect house selling prices and time on the market while controlling for brokerage firm-specific effects as well as supply and demand conditions that vary by neighborhood. Firm size effects disappear once firm specialization and agent characteristics are taken into account but geographic concentration by firms leads to higher selling prices. For individual agents, neither sex nor selling own listings affects price or selling time, but there are gains from partnering transactions across firms. Agents who specialize in listing properties obtain higher prices for their sellers while those who specialize in selling obtain lower prices for their buyers. Houses nearer to other transactions of an agent sell for higher prices. Finally, greater scale of listing and selling activity by an agent tends to lower selling price or lengthen the time on the market. Copyright Springer Science+Business Media, LLC 2007Real estate agents, Brokers, Brokerage, Housing, G24, R21, R31,

How do markets value relative house size in a neighborhood? The literature offers differing rationales: atypical houses sell for less, capitalization of property taxes penalizes larger and benefits smaller houses in mixed neighborhoods and conspicuous consumption reinforces the value of relatively larger houses and reduces the value of relatively smaller houses to consumers. Using a simultaneous price-liquidity model that controls for neighborhood supply and demand conditions, this article finds a dominant tax capitalization effect on price and marketing time that appears to override any extant atypicality or conspicuous consumption effects.Which does the market more highly value, the smallest house in a neighborhood of large houses or the largest house in a neighborhood of smaller houses? Real estate agents often give seemingly conflicting recommendations, arguing that the best deal is to buy into a nicer neighborhood by buying in on the low end while also maintaining that buying the largest house in a neighborhood gives more house for the money. And regardless of one's personal view, casual conversation about the answer to this question will quickly illustrate the wide range of opinions that exists.The academic literature also offers differing rationales on this point. One hypothesis attributable to Haurin (1988) is that atypical houses by definition do not fit the neighborhood and so are priced to sell for less. Another, originally developed by Hamilton (1976), is that the capitalization of the property tax for a given public service bundle penalizes larger houses and benefits smaller houses in mixed neighborhoods, thereby leading to differential capitalization effects on each. Yet another is that conspicuous consumption reinforces the value of relatively larger houses and reduces the value of relatively smaller houses to consumers. While these separate hypotheses are not mutually exclusive, they do lead to different answers to the question of relative house size and value.

This paper examines the relationship between the residential development sequence and land price. Inherent in the dynamics of residential development is that the first consumers face the greatest risk since they do not know with certainty what the neighborhood characteristics will be; subsequent consumers have more information. The model predicts that land prices will rise over time relative to the market; developers offer the first consumers discounted land prices to compensate them for the first-mover disadvantage. The empirical evidence indicates that this is indeed the case.