DOI: 10.5281/zenodo.18778210
title: Harmonized Spatiotemporal Dataset of Residential Real Estate Listings in Tegucigalpa, Honduras

description: A harmonized spatiotemporal dataset of residential real-estate listings for urban housing analysis

 



This dataset provides a harmonized spatiotemporal compilation of residential real-estate listings for Tegucigalpa, Honduras, covering benchmark years between 2010 and 2025. It is designed to support urban housing research, spatial analysis, and monitoring of Sustainable Development Goal 11 (Sustainable Cities and Communities).

The dataset consists of a spatially explicit GeoPackage (GPKG) containing neighborhood-linked residential property listings reconstructed from heterogeneous historical sources, including printed advertisements, archived web pages, and online property portals. Listings were standardized using a reproducible rule-based parsing and validation pipeline.

Records are harmonized across transaction type, property type, currency (USD), spatial identifiers, and temporal reference. Quality-control procedures include rule-based validation and statistical screening; potentially anomalous records are flagged rather than removed to preserve transparency.

The released dataset contains aggregated and standardized layers. Raw parsed listings are not publicly available due to source and licensing constraints. The full processing pipeline is openly available via GitHub and archived on Zenodo as a related resource.
description: This dataset provides a harmonized spatiotemporal compilation of residential real-estate listings in Tegucigalpa, Honduras, spanning benchmark years from 2010 to 2025. It consists of a spatially explicit GeoPackage containing standardized neighborhood-level residential property data reconstructed from heterogeneous historical sources. Listings were processed through a reproducible rule-based pipeline that harmonizes transaction types, property categories, currencies, spatial identifiers, and temporal labels. The dataset supports spatial-temporal housing market analysis and urban monitoring in data-scarce contexts.
description: Residential real-estate listings were reconstructed from heterogeneous historical sources, including printed advertisements, archived web pages, and online property portals. Raw listings were manually and semi-automatically extracted and converted into a canonical text format, then parsed using a reproducible, city-agnostic processing pipeline. Standardization procedures harmonized transaction types, property categories, currencies, spatial references, and temporal labels. Quality control was implemented through rule-based validation and statistical screening; potentially anomalous records were flagged rather than removed. Spatial integration was performed by linking cleaned listings to neighborhood-level polygon geometries and exporting the final dataset as a GeoPackage (GPKG)Optical character recognition (OCR) is not part of the pipeline; input listings were provided as structured text prior to parsing.

 
description: Data format: GeoPackage (GPKG)




Spatial reference system: Projected or geographic CRS defined at the layer level (EPSG code included in metadata)




Spatial units: Neighborhood-level polygon linkage with unique identifiers




Temporal resolution: Benchmark years (e.g., 2010, 2015, 2020, 2025) with monthly or near-monthly timestamps




Attributes: Standardized variables for transaction type, property type, price (USD), area, temporal reference, and quality-control flags




Quality control: Rule-based validation and statistical screening; anomalous records are flagged rather than removed




Software environment: Dataset generated using open-source Python and GIS tools; full processing pipeline is publicly available via the associated GitHub repository




File integrity: UTF-8 encoding; consistent field naming; reproducible exports
description: The dataset reflects advertised housing market conditions and does not capture informal transactions or completed sales