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Correlation Between Data Adjustment and Property Time on Market: Evidence from Jakarta Indonesia Riyanto, Edy; Prasetyo, Kristian Agung
Jurnal Ilmu Manajemen dan Ekonomika Vol. 18 No. 1 (2025): Jurnal Ilmu Manajemen dan Ekonomika, Vol. 18, No.1, December 2025
Publisher : Indonesia Banking School

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35384/jime.v18i1.850

Abstract

Property valuation in emerging markets often relies on asking prices due to limited access to verified transaction data. This reliance requires a data-type adjustment to reduce the gap between asking and transaction prices. Meanwhile, literature suggests a potential relationship between price concessions and time on market (TOM). This study aims to examine whether listing duration is significantly correlated with the magnitude of data-type adjustment in Jakarta’s residential property market. Using 331 verified transaction data from the Directorate General of State Assets (DJKN), the research applies descriptive statistics, chi-square tests, and polychoric correlation analysis. The results show that although 67.7% of properties were sold within six months, no significant correlation was found between TOM and data-type adjustment (r = 0.08, p = 0.74). Instead, the role of intermediaries such as brokers and agents appeared to have greater influence on narrowing the gap between asking and transaction prices. The findings indicate that the price–duration trade-off commonly reported in developed markets does not apply in Jakarta. This study highlights the importance of empirical evidence in determining adjustment practices and provides practical implications for valuers, brokers, and policymakers in emerging markets.
Reducing property valuation bias through random forests: Predicting prices for public asset optimization Riyanto, Edy; Alfianyah, Imanishi Dwi
Optimum: Jurnal Ekonomi dan Pembangunan Vol. 16 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/optimum.v16i1.13186

Abstract

This study applies supervised machine learning, specifically the Random Forest regression algorithm, to predict office rental prices in DKI Jakarta. A dataset was compiled via web scraping of online property listings, incorporating features such as location, office area, number of floors, lifts, parking capacity, and building grade. Data preprocessing involved handling missing values, removing outliers, applying one-hot encoding, and normalizing the data to ensure consistency. The model was developed using the CRISP-DM framework and evaluated through an 80:20 train-test split and 10-fold cross-validation. Performance metrics included Root Mean Squared Error (RMSE) and R². The Random Forest model achieved high accuracy, with cross-validation yielding an R² of 0.934 and an RMSE of Rp16.288 per m²/month. SHAP analysis revealed that lifts, floors, parking, office area, and building grade significantly influenced predictions. Bias analysis indicated a tendency to underestimate rents for grade B and C buildings. The model was also simulated to estimate rental values of underutilized government-owned offices, supporting asset optimization amid the planned capital relocation. These results demonstrate the potential of machine learning to improve valuation practices, reduce bias, and enhance decision-making in public asset management.