library(tidyverse)Here we do a domain dive on the PLUTO data and produce a data dictionary. Additionally we’ll reduce the number of columns to have more meaningful data in the subsequent merge.
The PLUTO data or “Primary Land Use Tax Lot Output” data file compiles information from the Departments of City Planning (DCP), Finance (DOF), Citywide Administrative Services (DCAS) and the Landmarks Preservation Commission (LPC) in one place, covering characteristics of the Tax Lots, Buildings and Geographic/Political/Administrative Districts.
If this information were used to calculate Market Total Value by the DOF, it should be in the DOF’s Property Valuation Assessment Data file, so our goal here is look for any features that are correlated to assessed Market Total Value which can help make an argument for whether a DOF’s assessment is too high or low.
I read the PLUTO README document April 2025 (25v1.1) and the only take away was that while normally a tax lot is a parcel of land, for condominiums, each condo is it’s own tax lot and for consistency and comparability between buildings, all condo tax lots in a given governing condo complex are consolidated together into one record.
Since we saved the subset PLUTO data from Code Chunk 3 as an .rds file it’s easy to load.
# Load subset PLUTO data
df <- readRDS("~/Documents/D698/PLUTO.rds")The PLUTO database has longitude and latitude so we could add a later-stage model that looks at nearby properties to reinforce Market Total Value assessments. I question this method in a city where a one-dollar pizza place is next to a $40 pizza place is next to $300 per person Omakase tasting menu, however it should have some impact, especially since propinquity is part of the verbal description of the DOF’s otherwise opaque methodology for assessing market value. Note, the longitude and latitude reported are WGS 84 (World Geodetic System).
Alternatively to the longitude and latitude, we have the x and y coordinates for the closest point within the lot to the lot’s centroid on the New York-Long Island State Plane coordinate system. We could center (0,0) to the means for interpretability, too.
I started to compile a fourth column for which department the PLUTO data came from to help distinguish which fields are duplicates however the Department of City Planning credited themselves for a lot of DOF fields and so we’ll proceed with a selection based on our domain dive.
Building Classes should map to either C or
D for our purposes with Class 2 properties, and we expect
whether the apartment is walk up or elevator to have a big impact on the
market value assessed.
C. WALK UP APARTMENTS
0. Three Families
1. Over Six Families Without Stores
2. Five to Six Families
3. Four Families
4. Old Law Tenements
5. Converted Dwelling or Rooming House
6. Cooperative
7. Over Six Families with Stores
8. Co-Op Conversion from Loft/Warehouse
9. Garden Apartments
M. Mobile Homes/Trailer Parks
D. ELEVATOR APARTMENTS
0. Co-op Conversion from Loft/Warehouse
1. Semi-fireproof (Without Stores)
2. Artists in Residence
3. Fireproof (Without Stores)
4. Cooperatives (Other Than Condominiums)
5. Converted
6. Fireproof with Stores
7. Semi-Fireproof with Stores
8. Luxury Type
9. Miscellaneous
| Column | Example | Description |
|---|---|---|
| borough | “MN” | Borough, ‘MN’ is for Manhattan |
| block | “1046” | Tax Block |
| lot | “23” | Tax Lot (consolidated for condos) |
| cd | “104” | Community district (59 in NYC and 12 in Manhattan, 104 is cd#4) |
| bct2020 | “1013900” | Census tract 2020 |
| bctcb2020 | “1.0139e+10” | Census block 2020 |
| ct2010 | “139” | Census tract 2010 |
| cb2010 | “5000” | Census block 2010 |
| schooldist | “02” | School district |
| council | “6” | City council district (#6 represented by Gale Brewer) |
| zipcode | “10019” | Zip code |
| firecomp | “E040” | Fire company (Engine 40) |
| policeprct | “18” | Police precinct |
| healthcenterdistrict | “15” | Health center district |
| healtharea | “4600” | Health area |
| sanitboro | “1” | Sanitation district boro |
| sanitdistrict | “04” | Sanitation district number |
| sanitsub | “3A” | Sanitation subsection |
| address | “315 WEST 55 STREET” | Address |
| zonedist1 | “R8” | Zoning District 1 |
| zonedist2 | NA | (Residence, Commercial or Manufacturing) |
| zonedist3 | NA | higher numbers are higher density |
| zonedist4 | NA | Additional ZD is for a lot divided by zoning boundary lines |
| overlay1 | NA | Commercial Overlay |
| overlay2 | NA | For when C-zoning is allowed in R-zoning |
| spdist1 | “CL” | Special Purpose District |
| spdist2 | NA | “CL” is for Clinton District |
| spdist3 | NA | Created to preserve residences from new development |
| ltdheight | NA | Limited height district |
| splitzone | “N” | Split boundary indicator |
| bldgclass | “D4” | Building Class |
| landuse | “03” | Land use category (03 is Multi-Family Elevator Buildings) |
| easements | “0” | Number of easements |
| ownertype | NA | Type of ownership code |
| ownername | “315 W 55TH OWNERS CORP” | Owner name |
| lotarea | “5725” | Lot area |
| bldgarea | “26959” | Total building floor area |
| comarea | “1000” | Commercial floor area |
| resarea | “25959” | Residential floor area |
| officearea | “1000” | Office floor area |
| retailarea | “0” | Retail floor area |
| garagearea | “0” | Garage floor area |
| strgearea | “0” | Storage floor area |
| factryarea | “0” | Factory floor area |
| otherarea | “0” | Other floor area |
| areasource | “2” | Total building floor area source code |
| numbldgs | “1” | Number of buildings |
| numfloors | “7” | Number of floors |
| unitsres | “42” | Residential Units |
| unitstotal | “44” | Total units |
| lotfront | “57” | Lot frontage |
| lotdepth | “100.42” | Lot depth |
| bldgfront | “57” | Building frontage |
| bldgdepth | “71” | Building depth |
| ext | “N” | Extension code |
| proxcode | “3” | Proximity code (3 means attached to neighboring buildings) |
| irrlotcode | “N” | Irregular lot code |
| lottype | “5” | Lot type (“5”: lot has frontage on only one street) |
| bsmtcode | “1” | Basement type/grade |
| assessland | “311850” | Assessed land value |
| assesstot | “3518550” | Assessed total value |
| exempttot | “17200” | Exempt total value |
| yearbuilt | “1945” | Year built |
| yearalter1 | “1972” | Year altered |
| yearalter2 | “0” | Year altered 2 |
| histdist | NA | Historic District Name |
| landmark | NA | Landmark Status |
| builtfar | “4.71” | Built floor area ratio (total building floor area/lot area) |
| residfar | “6.02” | Maximum allowable residential FAR (floor area ratio) |
| commfar | “0” | Maximum allowable commercial FAR (floor area ratio) |
| facilfar | “6.5” | Maximum allowable community facility FAR |
| borocode | “1” | Boro Code |
| bbl | “1010460023” | Borough, tax block & lot |
| condono | NA | Condominium number |
| tract2010 | “0139” | Census tract 2 |
| xcoord | “988531” | x coordinate in the NY-LI State Plane Coordinate System |
| ycoord | “218341” | y coordinate |
| zonemap | “8c” | Zoning Map # |
| zmcode | NA | Zoning Map Code |
| sanborn | “106W022” | Sanborn Map # |
| taxmap | “10403” | Tax Map # |
| edesignum | NA | E-designation number |
| appbbl | NA | Apportionment BBL (before the lot was apportioned into condos) |
| appdate | NA | Apportionment Date |
| plutomapid | “1” | PLUTO - DTM Base map indicator |
| firm07_flag | NA | 2007 Flood Insurance rate map indicator |
| pfirm15_flag | NA | 2015 preliminary flood insurance rate map indicator |
| version | “25v1.1” | version number |
| dcpedited | NA | changed by DCP (City Planning changed any field values for this tax lot) |
| latitude | “40.76597” | Latitude (WGS 84) |
| longitude | “-73.98455” | Longitude (WGS 84) |
| notes | NA | Notes (specific to Inwood |
Here we subset for the 32 fields we are interested. We’re primarily concerned with ascertaining relative locations of properties as well as fields that could affect value such as Landmark Status or possibly School District.
selected_vars <- c("block", "lot", "cd", "bct2020", "bctcb2020", "schooldist", "council", "firecomp", "policeprct", "healthcenterdistrict", "healtharea", "zonedist1", "spdist1", "ltdheight", "bldgclass", "landuse", "easements", "histdist", "landmark", "builtfar", "residfar", "commfar", "facilfar", "bbl", "condono", "tract2010", "xcoord", "ycoord", "firm07_flag", "pfirm15_flag", "latitude", "longitude")
df2 <- df[, selected_vars]Here we write the file for use in the subsequent merge file
# Write file
saveRDS(df2, file="pluto2.rds")