This repository contains data and codes that support the findings of the study.- PPD-EPC open dataset with the enriched spatial analyses scores and UPRN.- Batch Geocoding Notebook of PPD-EPC dataset with GeoPy - Here API- PyQGIS codes for proximity, terrain, and visibility spatial analyses.- Jupyter Notebook of Machine Learning algorithms for mass property valuation.

This dataset has been published by the Office of the Real Estate Assessor of the City of Virginia Beach and data.virginiabeach.gov. The mission of data.virginiabeach.gov is to provide timely and accurate City information to increase government transparency and access to useful and well organized data by the general public, non-governmental organizations, and City of Virginia Beach employees.

This file includes data pertaining to Residential Properties. The "ParcelNumber" field can be joined to the "ParcelNumber" field in the "Parcel Area Details" data set for mapping purposes (current parcels only). This data set is updated on a daily basis and reflects the Real Estate system as of the previous business day.

Note: Active and Retired parcels are included in this data set so joining this data set to the datasets: ‘Real Estate (Base Active)’ or ‘Parcel Area Details’ will not result in all parcels matching since ‘Real Estate (Base Active)’ and ‘Parcel Area Details’ only contain currently active parcels.Please refer to this Data Guide for details on how to access and join Real Estate data

This table contains the assessed values for current tax year and prior tax year for land and building for properties in Fairfax County. There is a one to one relationship to the parcel data. Refer to this document for descriptions of the data in the table.

All data are 2020 Census Tract (neighborhood) level five-year estimates from the U.S. Census Bureau American Community Survey from 2017 to 2021. Median household income earned in the past 12 months. Includes wage or salary income; net self-employment income; interest, dividends, or net rental or royalty income or income from estates and trusts; Social Security or Railroad Retirement income; Supplemental Security Income (SSI); public assistance or welfare payments; retirement, survivor, or disability pensions; and all other income. Median home value (an estimate of how much the property would sell for if it were for sale) for properties owned, being bought, vacant for sale, or sold but not occupied at the time of the survey. Data are based on values reported by property owners. Median real estate taxes (due to all taxing jurisdictions) for owner-occupied properties are based on taxes reported by homeowners to the Census Bureau in the American Community Survey from 2017 to 2021.

Data source is the Office of Tax and Revenue’s Computer-Assisted Mass Appraisal (CAMA) system. The CAMA system is used by the Assessment Division (AD) within the Real Property Tax Administration to value real estate for ad valorem real property tax purposes.The intent of this data is to provide a sale history for active properties listed among the District of Columbia’s real property tax assessment roll. This data is updated daily. The AD constantly maintains sale data, adding new data and updating existing data. Daily updates represent a “snapshot” at the time the data was extracted from the CAMA system, and data is always subject to change.

This City Owned Property data has been compiled from deeds, maps, assessor records, and other public records on file in the City of Hartford. The intent of this data layer is to depict a graphical representation of real property information relative to the planimetric features for the City of Hartford and is subject to change as a more accurate survey may disclose.

Air right lots are reflect a party’s right to construct an improvement above an existing area of land that is not owned by the constructor. They are a type of development right in real estate referring to the empty space about a property. These tax lot numbers start at 7000. There are approximately 704 air rights lots. Non-contiguous Air Rights Lots numbered in 8000 series can either be District owned Multifamily rental units or Existing Development Mixed (residential and commercial).Multifamily 8000 series lots can be proposed development projects that are inclusive of the Mayor’s Office Affordable/Public Housing Initiatives. Additionally, they can either be development sites that are owned by the District and the site is leased to developer. Due to financing and legal requirements, each set of government funded units are required to have separate parcel ID’s (SSL’s). All the units are rentals, none of the units will be for sale.Existing Development Mixed Use 8000 series lots are residential owner(s) that own both residential and commercial portions. The Lot split is done to ensure each party pays the appropriate real estate taxes assessed to each specific use. There is a master covenant lease outlining property access-rights-use between residential and commercial owner and lease holders. There is also a master lease related to the commercial space where the residential owner is the lease holder.

The percentage of residential properties that have received at least one housing code violation from the Baltimore City Department of Housing out of all properties. Properties whose fa?ade, structure, and/or surrounding area violate the City?s Housing Code are issued a notice and are considered open till the property is found in compliance. A property may receive multiple violations. Source: Baltimore Department of Housing and Community Development Years Available: 2010, 2011, 2012, 2013, 2015, 2016, 2017, 2018, 2019, 2020, 2023

This dataset visualises the spatial distribution of the rental value in Amsterdam between 1647 and 1652. The source of rental value comes from the Verponding registration in Amsterdam. The verponding or the ‘Verpondings-quohieren van den 8sten penning’ was a tax in the Netherlands on the 8^th penny of the rental value of immovable property that had to be paid annually. In Amsterdam, the citywide verponding registration started in 1647 and continued into the early 19^th century. With the introduction of the cadastre system in 1810, the verponding came to an end.

The original tax registration is kept in the Amsterdam City Archives (Archief nr. 5044) and the four registration books transcribed in this dataset are Archief 5044, inventory 255, 273, 281, 284. The verponding was collected by districts (wijken). The tax collectors documented their collecting route by writing down the street or street-section names as they proceed. For each property, the collector wrote down the names of the owner and, if applicable, the renter (after ‘per’), and the estimated rental value of the property (in guilders). Next to the rental value was the tax charged (in guilders and stuivers). Below the owner/renter names and rental value were the records of tax payments by year.

This dataset digitises four registration books of the verponding between 1647 and 1652 in two ways. First, it transcribes the rental value of all real estate properties listed in the registrations. The names of the owners/renters are transcribed only selectively, focusing on the properties that exceeded an annual rental value of 300 guilders. These transcriptions can be found in Verponding1647-1652.csv. For a detailed introduction to the data, see Verponding1647-1652_data_introduction.txt.

Second, it geo-references the registrations based on the street names and the reconstruction of tax collectors’ travel routes in the verponding. The tax records are then plotted on the historical map of Amsterdam using the first cadaster of 1832 as a reference. Since the geo-reference is based on the street or street sections, the location of each record/house may not be the exact location but rather a close proximation of the possible locations based on the street names and the sequence of the records on the same street or street section. Therefore, this geo-referenced verponding can be used to visualise the rental value distribution in Amsterdam between 1647 and 1652. The preview below shows an extrapolation of rental values in Amsterdam. And for the geo-referenced GIS files, see Verponding_wijken.shp.

GIS specifications:

Coordination Reference System (CRS): Amersfoort/RD New (ESPG:28992)

Historical map tiles URL (From Amsterdam Time Machine)

NB: This verponding dataset is a provisional version. The georeferenced points and the name transcriptions might contain errors and need to be treated with caution.

Contributors

• Historical and archival research: Weixuan Li, Bart Reuvekamp
• Plotting of geo-referenced points: Bart Reuvekamp
• Spatial analysis: Weixuan Li
• Mapping software: QGIS
• Acknowledgements: Virtual Interiors project, Daan de Groot

This is a comprehensive collection of tax and assessment data extracted at a specific time. The data is in CSV format. A data dictionary (pdf) and the current tax rate book (pdf) are also included.

2019 Real Estate Appraisal Data for Buncombe County, NC. This data is an export from the CAMA database and contains general data on size, value and status of residential buildings

Table provided nightly from Real Estate Assessments database to GIS. Table is Prince William County tax parcels current owner and other attributes provided to GIS from Real Estate Assessments database through a nightly process. Data is current as close of business the previous day.

Price inflation has outbalanced the income of residents and buyers in major post-industrial city-regions, and real estate has become an important driver of these inequalities. In a context of a resilient inflation of home values during the last two decades in the greater Paris Region, it is critical to examine housing price dynamics to get a better understanding of socioeconomic segregation. This paper aims at presenting spatial analysis of the dynamics of segregation pertaining to inflation, analyzing price and sellers and buyers data. Using interpolation techniques and multivariate analysis, the paper presents a spatial analysis of property-level data from the Paris Chamber of Notaries (1996-2012) in a GIS (159,000 transactions in suburban areas, single family homes only). Multivariate analysis capture price change and local trajectories of occupational status, i.e. changes in balance between inward and outward flows of sellers and buyers. We adopt a method that fits the fragmented spatial patterns of suburbanization. To do so, we remove the spatial bias by means of a regular 1-km spatial grid, interpolating the variables within it, using a time-distance matrix. The main results are threefold. We document the spatial patterns of professionalization (a rise of executives, intermediate occupation and employees) to describe the main trends of inward mobility in property ownership in suburbs, offsetting the outward mobility of retired persons. Second, neighborhood trajectories are related the diverging patterns of appreciation, between local contexts of accumulation with a growth of residential prices, and suburbs with declining trends. The maturity of suburbanization yields a diversified structure of segregation between the social groups, that do not simply oppose executives vs. blue collar suburbs. A follow-up research agenda is finally outlined.

Amherst, MA Property Lines.Metadata is available at http://gis.amherstma.gov/data/metadata/parcels.htm

This table contains property sales information including sale date, price, and amounts for properties within Fairfax County. There is a one to many relationship to the parcel data. Refer to this document for descriptions of the data in the table.

Fiscal Office Appraisal 2018 Neighborhoods - Commercial and Residential Last published 4/23/2019

2022 Real Estate Appraisal Data for Buncombe County, NC. This data is an export from the CAMA database and contains a break down of buildings into the types of areas (porches, finished areas, garages)