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Layer: Housing Market Typology 2014 (ID: 44)

Parent Layer: Housing Market Typology

Name: Housing Market Typology 2014

Display Field: GeoID10

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Description: Field Name Alias DescriptionGeoID10 Geoid10 United States Census Bureau 2010 block group unique identifierClusterLet Market Cluster Type The MVA market type derived from TRF's MVA cluster analysis.CSP1214_CI Count of residential home sales between 2012 and 2014Q2 The count of sales 2012 and 2014Q2, values under $1,000 were filtered out as non-arms length transactions. Residential sales were identified by usegroup in ('R','M','U') which is the "residential", "apartment", or "condo" designation, respectively, from the City of Baltimore parcel dataset.VSP1214_CI Coefficient of variance in residential home sales price between 2012 and 2014Q2 Coefficient of variance sales price was calculated by dividing the standard deviation of sales price within blockgroups by the average of sales price within blockgroups. Residential sales were identified by usegroup in ('R','M','U') which is the "residential", "apartment", or "condo" designation, respectively, from the City of Baltimore parcel dataset.MSP1214_CI Median residential home sales price between 2012 and 2014Q2 The median residential home sales price between 2012 and 2014Q2, values under $1,000 were filtered out as non arms length transactions. Residential sales were identified by usegroup in ('R','M','U') which is the "residential", "apartment", or "condo" designation, respectively, from the City of Baltimore parcel dataset.CVAC_14_CI Count of vacant housing parcels, 2014 Count of residential parcels that are vacant in September 2014, provided from the City of Baltimore .PVac_14_CI Vacant housing parcels as a % of all residential parcels, 2014 Percent of housing parcels that are vacant in September 2014, calculated by taking the count of vacant housing parcels (CVAC_14_CI) and dividing it by the count of residential parcels from the City of Baltimore.CVaLT14_CI Count of vacant lots, 2014 Count of residential parcels that are vacant land in November 2014, provided from the City of Baltimore. Areas with steep slope and with edges along the city boundary were filtered out by planning staff to create this updated variable.PVlot14_CI Vacant residential lots as a % of all residential parcels, 2014 Percent vacant residential parcels, calculated by taking the count of vacant lots (CVaLT14_CI) and dividing it by the count of residential parcels from the City of Baltimore parcel dataset.CFC1214_CI The count of residential foreclosure filings, 2012-2014Q2 The count of residential foreclosures filings, 2012-2014Q2, from the City of Baltimore dataset.PFcHu1214 Foreclosure filings as a % of all residential parcels 2012-2014Q2 The percent of residential foreclosures was calculated by dividing the count of residential foreclosures filings (cFC1214_CI) by the count of residential parcels from the City of Baltimore parcel dataset.CHUOO_14_C Count of owner occupied housing units in 2014 Count of housing units that are owner occupied, from the City of Baltimore parcel dataset, 2014.CHUO_14_Ci Count of occupied housing units in 2014 Count of all housing units that are occupied, from the City of Baltimore parcel dataset, 2014.PHOO_Hu_14 Owner-occupied units as a % of all occupied housing units, 2014 Percent of Housing Units that are Owner Occupied, from the City of Baltimore parcel dataset, 2014.PCIn_14_CI Commercial/Industrial land area as a % of total land area, 2014 Percent commercial and industrial land area calculated from the City of Baltimore parcel dataset. This variable was calculated by summing the area of commercial and industrial parcels (when usegroup like 'C' or like 'I') divided by the sum of the area of all parcels, 2014.CPa1214_gt Count of building permits < $10,000, 2012- 2014Q2 The count of parcels whose sum of building permits > $10,000, from 2012-2014Q2. Permits during the time period are tagged to each parcel and then aggregated. Parcels which have a combined permit value greater than $10,000 are then included in this count.PPa1214_gt Building permits as a % of all residential parcels, 2012 - 2014Q2 The count of parcels whose sum of building permits > $10,000 (CPa1214_gt) divided by the count of total residential parcels.LandSqMile Land area in square miles, 2014 The land area occupied by the 2010 block group, excluding water area, as calculated by TRF.HuSQmi14CI Housing units per square mile, 2014 The count of housing units in 2014 from the City of Baltimore parcel dataset (CHU_14_CI) divided by the land area in square miles, 2014 (LandSqMile).CHURO_14_C Count of renter occupied housing units in 2014 Count of housing units that are renter occupied, from the City of Baltimore parcel dataset, 2014.CHU_14_CI Count of housing units in 2014 The count of housing units in 2014 from the City of Baltimore parcel dataset._____________________________________________Our Normative Assumptions when Analyzing Markets: •Public subsidy is scarce and it alone cannot create a market; •Public subsidy must be used to leverage, or clear the path, for private investment; •In distressed markets, invest into strength (e.g., major institution of place, transportation hub, environmental amenities) – “Build from Strength”; •All parts of a city are customers of the services and resources that it has to offer; •Decisions to invest and/or deploy governmental programs must be based on objectively gathered data and sound quantitative and qualitative analysis. Preparing the MVA1.Take all of the data layers and geocode to Census block groups.2.Inspect and validate those data layers.3.Using a statistical cluster analysis, identify areas that share a common constellation of characteristics.4.Map the result.5.Visually inspect areas of the City for conformity with the statistical/spatial representation.6.Re-solve and re-inspect until we achieve an accurate representation.Please note that the following block groups were estimated based on data and on the ground validation. An associated shapefile with just these block groups is inclued in the original MVA map package, with cross hatching to note their status as estimated.geo_bounda in (245101202022, 245101304001, 245101307001, 245101601004, 245101301004, 245100401002, 245100402001, 245100401001, 245102604013, 245102801021, 245101202025, 245101206003, 245101302004, 245101601002, 245102604012, 245102604011 ,245102805003 ,245102805004, 245102803011, 245102805001).

Copyright Text: The Reinvestment Fund Department of Policy Solutions The Reinvestment Fund builds wealth and opportunity for low-wealth communities and low and moderate income individuals through the promotion of socially and environmentally responsible development.

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