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All Journal Jurnal Manajemen Keuangan Publik Jurnal Pajak Indonesia (Indonesian Tax Review) Jurnal Pajak dan Keuangan Negara (PKN) Ilomata International Journal of Management Ilomata International Journal of Social Science Jurnal Pemberdayaan Masyarakat Indonesia (JPMI) Jurnal Ilmu Manajemen & Ekonomika Jurnal Manajemen Aset dan Penilaian Jurnal Nusantara Mengabdi Optimum: Jurnal Ekonomi dan Pembangunan
Edy Riyanto
Politeknik Keuangan Negara (PKN) STAN
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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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.
Estimasi Harga Sewa Ruang Perkantoran menggunakan Machine Learning di Provinsi DKI Jakarta Imanishi Dwi Alfiansyah; Edy Riyanto
JURNAL MANAJEMEN KEUANGAN PUBLIK Vol 10 No 1 (2026): Ekonomi Publik, Kebijakan Fiskal/Negara, dan Pembangunan Daerah
Publisher : Polytechnic of State Finance STAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31092/jmkp.v10i1.3525

Abstract

Harga sewa ruang perkantoran sangat dipengaruhi oleh beberapa faktor seperti atribut dari properti, kondisi permintaan dan penawaran pasar, dan persepsi pelaku pasar dalam mengambil keputusan. Perkembangan teknologi saat ini menuntut semua hal dapat diolah dengan cepat, salah satunya yaitu, penilaian properti. Penelitian ini memanfaatkan teknik web scraping untuk menentukan estimasi harga sewa ruang perkantoran yang bertujuan untuk mengobservasi akurasi dari model algoritma dan mengestimasi harga sewa ruang perkantoran beserta tingkat akurasinya. Menggunakan 292 dataset, penelitian ini mencoba mengobservasi dan melakukan estimasi dengan teknik web scraping yang dikombinasikan dengan model random forest pada machine learning melalui split data approach dan cross validation approach. Model melalui cross validation approach mampu menunjukan akurasi yang lebih baik yaitu sebesar 93,4%, sedangkan akurasi estimasi harga sewa ruang perkantoran menghasilkan nilai yang tidak berbeda jauh dengan data pasarnya dimana terdapat skor RMSE sebesar Rp 16.288.
Reducing property valuation bias through random forests: Predicting prices for public asset optimization Edy Riyanto; Imanishi Dwi Alfianyah
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.
Methodological and Behavioural Drivers of Valuation Decision-Making in Property Markets: A Systematic Literature Review Edy Riyanto; Prayudi Nugroho
Ilomata International Journal of Management Vol. 7 No. 3 (2026): July 2026
Publisher : Yayasan Sinergi Kawula Muda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61194/ijjm.v7i3.2274

Abstract

Property valuation is a core function in organizational asset management, directly informing decisions related to investment, financing, and strategic resource allocation. Despite growing adoption of analytical models and automated valuation technologies, outcomes remain substantially influenced by professional judgement, organizational processes, and institutional constraints. Prior reviews have examined either methodological advances—such as Abidoye and Chan's (2017) review of ANN-based valuation models and Glumac and Des Rosiers' (2018) examination of big data applications—or behavioural factors in isolation (e.g., Gallimore, 1996; Diaz & Hansz, 1997), leaving an integrative gap this study addresses. Following the PRISMA 2020 framework, 42 peer-reviewed articles were selected from Scopus, Web of Science, and Emerald Insight (March 2024) and analyzed through three-stage thematic analysis. Five themes emerged: (1) methodological evolution in valuation practice, (2) professional judgement and organizational decision-making, (3) behavioural bias in appraisal management, (4) technological transformation through automated valuation systems, and (5) institutional governance of valuation standards. The resulting conceptual framework, derived inductively from thematic coding of the 42 included studies, conceptualizes valuation as a hybrid decision-making process where scientific and managerial-behavioural dimensions—moderated by institutional and technological context—jointly shape valuation outcomes. This study contributes by providing the first systematic integration of methodological and behavioural perspectives in property valuation, offering actionable insights for asset managers, financial institutions, and policymakers seeking to enhance valuation reliability in data-driven property markets.
Co-Authors Adhipradana Prabu Swasito Aditya Wirawan, Aditya Adityawan Rizqi Nurdianto Aniek Juliarin Budi Purnomo Dhian Adhetiya Safitra Eri Wahyudi Hanik Susilawati Muamarah Imanishi Dwi Alfiansyah Imanishi Dwi Alfianyah Khofifah Nila Juliana Kristian Agung Prasetyo Kristian Agung Prasetyo, Kristian Agung Murwanto, Nugroho Yoni Nurbiyanto Prayudi Nugroho Prayudi Nugroho, Prayudi Teguh Warsito Vissia Dewi Haptari, Vissia Dewi Warlan