Leakage-aware multimodal GeoAI for residential rent valuation: evidence from Arabic property listings in four Saudi cities
Year: 2026
Type: preprint
Abstract: Automated valuation models (AVMs) increasingly combine structured property attributes with listing text and location, yet their reported accuracy depends on how the data are cleaned and partitioned. This study evaluates a multimodal GeoAI workflow for annual villa rents in Riyadh, Jeddah, Dammam and Al-Khobar using public Aqar listings that pair structured attributes with Arabic descriptions. Listings were linked to a national district gazetteer (98.5% of the analysis sample matched), from which district centroids, district area, distance to the city centre and an inverse-distance spatial lag of neighbouring district rents were derived. A data audit showed that 2,197 of the 3,718 published records (59.1%) were exact duplicates, rising to 86–92% in Dammam and Al-Khobar. Gradient boosting with structured, text and geospatial inputs was compared with hedonic and random-forest baselines under four validation designs. With duplicates retained, random cross-validation gave a median absolute percentage error (MdAPE) of 1.5% and an R² of 0.82 on log rent; after de-duplication these became 15.4% and 0.60, and under district-grouped validation 19.3% and 0.55. Under district-grouped validation, geospatial context reduced mean absolute error by 20.6% (95% district-bootstrap interval 14.6–26.1%) relative to structured attributes alone and removed significant residual autocorrelation in Riyadh (Moran’s I from 0.40 to − 0.01). Text reduced error by 6.2% on its own but added no significant gain once geospatial context was present. Split-conformal intervals reached 91.6% coverage at the nominal 90% level, while leave-one-city-out transfer degraded all models (MdAPE 26–34%). De-duplication and spatially grouped validation should accompany reported AVM accuracy.
Source: Research Square
Author: Othman Almashaqba
Institution: Applied Science Private University
Language: English
Topic: Housing Market and Economics
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7225178002