Abstract
In the housing market, the advertised list price significantly influences the final sale price and the duration a property spends on the market. With the uncertainties and complexities of the housing market, the list price can be a difficult and critical decision for home sellers. This paper develops a theoretical model for an optimal list price strategy with an information learning process to ensure a maximum expected return and a discussion of the ideal vacancy rate. The optimal list price strategy is composed of factors such as the frequency of list price revisions, the best timing to revise the price, and the value of a sequence of list prices and varies according to the market situation, and patience and prior knowledge of the seller. The results of the application in Tokyo 23 wards imply that there is an excess of houses in the sale market and potential for home sellers to improve their list price strategy and the learning process in order to shorten the time a property spends on the market without losing any return.
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