DOI: 10.5281/zenodo.18226144
title: A harmonized spatiotemporal dataset of residential real-estate listings for urban housing analysis: Tegucigalpa (2010–2025)

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

 



This dataset provides a harmonized spatiotemporal compilation of residentialreal-estate listings designed to support urban housing analysis and monitoringof Sustainable Development Goal 11 (Sustainable Cities and Communities).

The dataset consists of a spatially explicit GeoPackage (GPKG) containingstandardized residential property listings for Tegucigalpa, Honduras, coveringselected benchmark years between 2010 and 2025. Listings were reconstructedfrom heterogeneous historical sources—including printed advertisements,archived web pages, and online property portals—using a reproducible,city-agnostic parsing and validation pipeline.

All records were harmonized in terms of transaction type, property type,currency, spatial reference, and temporal labeling. Quality control proceduresinclude rule-based validation and anomaly detection; potentially problematicrecords are flagged rather than removed to preserve transparency.

The dataset is intended for use in urban planning research, housing marketanalysis, spatial-temporal studies, and SDG-11 indicator development. Theassociated software pipeline used to generate this dataset is openly availablevia GitHub and archived on Zenodo as a related resource.
description: This dataset provides a harmonized spatiotemporal compilation of residential real-estate listings to support urban housing analysis and Sustainable Development Goal 11 monitoring. It consists of a spatially explicit GeoPackage (GPKG) containing standardized residential property listings for Tegucigalpa, Honduras, covering selected benchmark years between 2010 and 2025. Listings were reconstructed from heterogeneous historical sources and processed using a reproducible parsing, standardization, and quality-control pipeline. The dataset enables spatial-temporal analysis of housing markets, urban development patterns, and affordability dynamics in data-scarce urban 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).
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 do not capture informal transactions or completed sales.