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particular property located at a given address. These data provide us with a variety of <br />property characteristics, such as the use -code (single-family, multi -family, commercial, etc.), <br />the year the building was built, and the number of units in the structure. For each property, <br />the data also details its transaction history since 1988, including transaction prices, as well <br />as the buyer and seller names. Again, we assess the quality of the matching procedure <br />by comparing the distribution of the year buildings were built across census tracts among <br />addresses listed as occupied in Infutor versus the 1990 and 2000 censuses. Figures A.3 and <br />A.4 show the age distribution of the occupied stock by census tract. In both of the years <br />1990 and 2000, our R-squareds are high and we often cannot reject a slope of one. ' This <br />highlights the extremely high quality of the linked Infutor-DataQuick data, as the addresses <br />are clean enough to merge the outside data source DataQuick and still manage to recover <br />the same distribution of building ages as reported in both 1990 and 2000 Censuses. <br />To measure whether Infutor residents were owners or renters of their properties, we <br />compare the last names of the property owners list in DataQuick to the last names of the <br />residents listed in Infutor. Since property can be owned in trusts, under a business name, or <br />by a partner or spouse with a different last name, we expect to under -classify residents as <br />owners. Figures A.5 and A,6 plot the Infutor measure of ownership rates by census tract in <br />1990 and 2000, respectively, against measures constructed using the 1990 and 2000 censuses. <br />In 1990 (2000), a one percentage point increase in the owner -occupied rate is leads to a <br />0.43 (0.56) percentage point increase in the ownership rate measured in Infutor. Despite the <br />under counting, our cross-sectional variation across census tract matches the 1990 and 2000 <br />censuses extremely well, with R-squareds over 90% in both decades. This further highlights <br />the quality of the Infutor data. <br />Next we match each address to its official parcel number from the San Francisco Assessor's <br />office. Using the parcel ID number from the Secured Roll data, we also merge with any <br />6Since year built comes from the Census long form, these data are based only on a 20% sample of the <br />true distribution of building ages in each tract, creating measurement error that is likely worse in the census <br />than in the merged Infutor-DataQuick data. <br />9 <br />