{ "currentVersion": 10.71, "id": 44, "name": "Housing Market Typology 2014", "type": "Feature Layer", "description": "Field Name\tAlias\t\t\t\t\t\t\t\t\t\tDescriptionGeoID10\tGeoid10\t\t\t\t\t\t\t\t\t\tUnited States Census Bureau 2010 block group unique identifierClusterLet\tMarket Cluster Type\t\t\t\t\t\t\t\tThe MVA market type derived from TRF's MVA cluster analysis.CSP1214_CI\tCount of residential home sales between 2012 and 2014Q2\t\t\t\tThe 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\tCoefficient of variance in residential home sales price between 2012 and 2014Q2\t\tCoefficient 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\tMedian residential home sales price between 2012 and 2014Q2\t\t\tThe 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\tCount of vacant housing parcels, 2014\t\t\t\t\t\tCount of residential parcels that are vacant in September 2014, provided from the City of Baltimore .PVac_14_CI\tVacant housing parcels as a % of all residential parcels, 2014\t\t\t\tPercent 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\tCount of vacant lots, 2014\t\t\t\t\t\t\t\tCount 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\tVacant residential lots as a % of all residential parcels, 2014\t\t\t\tPercent 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\tThe count of residential foreclosure filings, 2012-2014Q2\t\t\t\tThe count of residential foreclosures filings, 2012-2014Q2, from the City of Baltimore dataset.PFcHu1214\tForeclosure filings as a % of all residential parcels 2012-2014Q2\t\t\tThe 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\tCount of owner occupied housing units in 2014\t\t\t\t\tCount of housing units that are owner occupied, from the City of Baltimore parcel dataset, 2014.CHUO_14_Ci\tCount of occupied housing units in 2014\t\t\t\t\t\tCount of all housing units that are occupied, from the City of Baltimore parcel dataset, 2014.PHOO_Hu_14\tOwner-occupied units as a % of all occupied housing units, 2014\t\t\tPercent of Housing Units that are Owner Occupied, from the City of Baltimore parcel dataset, 2014.PCIn_14_CI\tCommercial/Industrial land area as a % of total land area, 2014\t\t\t\tPercent 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\tCount of building permits < $10,000, 2012- 2014Q2\t\t\t\t\tThe 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\tBuilding permits as a % of all residential parcels, 2012 - 2014Q2\t\t\tThe count of parcels whose sum of building permits > $10,000 (CPa1214_gt) divided by the count of total residential parcels.LandSqMile\tLand area in square miles, 2014\t\t\t\t\t\t\tThe land area occupied by the 2010 block group, excluding water area, as calculated by TRF.HuSQmi14CI\tHousing units per square mile, 2014\t\t\t\t\t\t\tThe 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\tCount of renter occupied housing units in 2014\t\t\t\t\tCount of housing units that are renter occupied, from the City of Baltimore parcel dataset, 2014.CHU_14_CI\tCount of housing units in 2014\t\t\t\t\t\t\tThe count of housing units in 2014 from the City of Baltimore parcel dataset._____________________________________________Our Normative Assumptions when Analyzing Markets: \u2022Public subsidy is scarce and it alone cannot create a market; \u2022Public subsidy must be used to leverage, or clear the path, for private investment; \u2022In distressed markets, invest into strength (e.g., major institution of place, transportation hub, environmental amenities) \u2013 \u201cBuild from Strength\u201d; \u2022All parts of a city are customers of the services and resources that it has to offer; \u2022Decisions 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).", "geometryType": "esriGeometryPolygon", "sourceSpatialReference": { "wkid": 102685, "latestWkid": 2248 }, "copyrightText": "The Reinvestment Fund Department of Policy Solutions \nThe 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.", "parentLayer": { "id": 40, "name": "Housing Market Typology" }, "subLayers": [], "minScale": 0, "maxScale": 0, "drawingInfo": { "renderer": { "type": "uniqueValue", 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