Repeat Sales analysis aggregates changes in value and statistical means for properties sold more than once during a specified period of time in a given geographic area. For example, in a zip or postal code area, estimate market-level housing price changes. If an individual property has not been substantially changed since its last sale, this analysis matches each pair of sales transactions (thus the name “repeat sales”). The amount of appreciation (or depreciation) is calculated from the time of the first sale to the second and so on, providing an estimate of the overall appreciation of that local housing market during that time period. The larger the number of available sales pairs, the more statistically reliable the estimate of overall housing price trends will be. Because this analysis is based on identifying properties where more than one sale has occurred, the challenge is to identify enough observations to provide a meaningful index of housing values, while keeping to as small a geographic area as possible. A repeat sales index may also overestimate market appreciation if the data contains pairs of sales in which the second sales price reflects substantial improvements (or other alterations) made to the property after the first sale. On the other hand, repeat sales indices can and do provide very useful valuation estimates in jurisdictions where the data is insufficient to support hedonic models. In addition, they may prove more accurate in tracking housing values for the houses that a hedonic model may struggle with (especially those subject to extreme positive or negative influences) when a prior sale is known on the property.