Property Sales Data

The data sets, which were purchased from the Maricopa County Assessor contain sale prices, property

homes and condos/townhome County, Arizona.8 _{This study utilize}

8

Condo housing type includes some townhome type Assessor. Thus, the term “condo

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 U n it C o u n ts Housing Booms SFH Sales Volume (Left Axis)

Single Family Housing Median Price for Phoenix: 2003-2008. Source: Information Market, 2009.

Sales Data

The data sets, which were purchased from the Maricopa County Assessor

property characteristics, and location information for single family /townhomes that sold during 2005 and 2008 in Phoenix, Maricopa This study utilizes data in different years to capture how the effects of

Condo housing type includes some townhome types in the property information of Maricopa condo” in this study includes some townhome types.

$0 $20,000 $40,000 $60,000 $80,000 $100,000 $120,000 $140,000 $160,000 $180,000 $200,000 $220,000 $240,000 $260,000 $280,000 $300,000 Period (2003-2008) Sale Price Housing Busts Housing Booms Sales Volume SFH Median Price (Right Axis) SFH Foreclosure Counts (Left Axis)

The data sets, which were purchased from the Maricopa County Assessor Office, characteristics, and location information for single family that sold during 2005 and 2008 in Phoenix, Maricopa data in different years to capture how the effects of

nformation of Maricopa County Sale Price SFH Sales Volume SFH Median Price SFH Foreclosure Filings

foreclosures on nearby property values may vary over the housing cycles. To test for differences in differing housing market cycles, it separately examines the foreclosure effects on sales that took place during 2005 as a housing boom year and on sales that took place during 2008 as a housing bust year. Thus, this study created four sample data sets of existing home sales: two consist of single family homes and condos sold in 2005, representing an upward market scenario; the other two contain single family homes and condos sold in 2008, representing a recent downside market scenario in the Phoenix housing market.

Matching sales and property information data to the corresponding geographic file is a key to this study since spatial variables are generated with GIS. After GIS procedures, the sales and foreclosure filings are placed in real space and sorted by using MS Access software.

For the study sample, this study uses only sale samples of existing housing units rather than newly built housing units and residential zoning with similar housing density and property types within the study area. These single family home sales and condo sales would be representative housing property sales in neighborhoods and be used as comparable benchmarks for residential valuation.

For typical home sales, this study is limited to typical home sales by arm’s length transactions, which have never been under foreclosure in the two years prior to the transaction. However, for distressed sales associated with previous foreclosures, this study is limited to home sales that had at least one foreclosure filing in the two years prior to the 2005 housing sale samples and the 2008 housing sale samples in the Phoenix

area. Distressed sales related to the foreclosure process not only include non-typical transactions such as short sales, foreclosure sales, bank owned sales, but also include properties canceled in the foreclosure process and sold later as urgent sales. All sales and foreclosure data originate from deeds, not mortgage information; thus, distressed sales associated with foreclosure are at the point that new owners already have taken over ownership of the property.

The procedures of cleaning data (removing inconsistent and incomplete observations such as missing structural characteristics, transfers, grants, quick claims, etc.) and eliminating outliers (consisting of those in the top and bottom 2% of sale prices) are to avoid erroneously recorded or atypical transactions from the sample data.9

Full housing samples in this study consist of all single family homes and condos which faced a foreclosure in the two years prior to the transaction and single family homes and condos that were sold by arm’s length transactions in Phoenix, Arizona during 2005 and 2008.

Figure 4.4 illustrates that the 2005 single family home sample consists of 2,214 distressed sales and 28,601 typical sales. The 2008 single family home sample consists of 6,730 distressed sales and 6,155 typical sales. The 2005 condo sample consists of 256 distressed sales and 5,949 typical sales. The 2008 condo sample consists of 538

9

If the condo transactions on the ground level and on upper level with different ownership (multi-floor semi-detached homes) occur in the same year, this study just includes an average price for them since GIS recognizes the location of property based on X-Y coordinate and codes one time for the same location. Duplicated condo transactions on the same property with multiple stories cause trouble in constructing a spatial weight matrix. See the detailed technological issues of spatial weight matrices in section 5.2.3.2. In addition, if there are repeated transactions on a single family home or condo during sample period, only the last transactions in the year are included in the sample data set and then coded in GIS.

distressed sales and 1,465 typical sales.

Figure 4.4. Home Sales Samples in 2005 and 2008.