Sistem Peringatan Dini Kekosongan Kantor dengan Deteksi Anomali Deret Waktu: Studi Kasus Perkantoran Jakarta
DOI:
https://doi.org/10.24002/jbi.v17i2.15129Keywords:
office vacancy, sistem peringatan dini, deteksi anomali, change-point detection, informatika perkotaanAbstract
Vacancy perkantoran di kota-kota pasar berkembang menunjukkan dinamika nonlinier yang tidak dapat ditangkap secara memadai melalui pemantauan deskriptif konvensional. Artikel ini mengusulkan sistem peringatan dini (Early Warning System/EWS) yang interpretatif untuk mendeteksi 'tipping point' kekosongan kantor dengan mengintegrasikan deteksi anomali deret waktu dan penyaringan perubahan struktur. Kerangka ini diterapkan pada data pasar kantor kuartalan Jakarta CBD periode 2024Q1–2025Q3 menggunakan dua indikator komplementer: vacancy rate (%) dan net absorption (sqm/kuartal). Hasil empiris menunjukkan trajektori perbaikan okupansi secara bertahap: vacancy menurun dari 26,4% (2024Q1) menjadi 23,7% (2025Q3), disertai deselerasi penyerapan pada 2025 (dari 53.500 sqm pada 2024Q3 menjadi 23.700 sqm pada 2025Q3). Kerangka ini dirancang untuk dapat dioperasikan secara rolling kuartalan dan dapat diperluas ke pemantauan multi-kota.
Office vacancy in emerging market cities exhibits non-linear dynamics that conventional descriptive monitoring fails to capture adequately. This paper proposes an Early Warning System (EWS) designed to detect vacancy 'tipping points' by integrating time-series anomaly detection with structural-change screening. The framework is applied to quarterly Jakarta Central Business District (CBD) office market data spanning 2024–2025Q3, using two complementary indicators: vacancy rate (%) and net absorption (sqm/quarter). Empirical results reveal a gradual occupancy improvement trajectory, with vacancy declining from 26.4% (2024Q1) to 23.7% (2025Q3), alongside a notable absorption deceleration in 2025 (from 53,500 sqm in 2024Q3 to 23,700 sqm by 2025Q3). Findings contribute to the nascent literature on data-driven real estate market surveillance, offering a methodological template for asset managers, portfolio investors, and urban planning regulators in data-constrained emerging market settings.
References
[1] J. M. Barrero, N. Bloom, and S. J. Davis, "Why working from home will stick," National Bureau of Economic Research, Cambridge, MA, Working Paper 28731, Apr. 2021, doi: 10.3386/w28731.
[2] O. Oladiran, P. Hallam, and L. Elliott, “The COVID-19 pandemic and office space demand dynamics”, IJSPM, vol. 27, no. 1, pp. 35–49, Mar. 2023, doi: 10.3846/ijspm.2023.18003.
[3] Y. Liu and Q. Su, "Hybrid work and commercial real estate: Implications for office demand in the post-pandemic era," Journal of Real Estate Research, vol. 45, no. 2, pp. 145–172, 2023.
[4] S. S. Rosenthal, W. C. Strange, and J. A. Urrego, "JUE insight: Are city centers losing their appeal? Commercial real estate, urban spatial structure, and COVID-19," Journal of Urban Economics, vol. 127, p. 103381, Jan. 2022, doi: 10.1016/j.jue.2021.103381.
[5] J. Liu, D. Yang, K. Zhang, H. Gao, and J. Li, "Anomaly and change point detection for time series with concept drift," World Wide Web, vol. 26, pp. 3229–3252, Jul. 2023, doi: 10.1007/s11280-023-01181-z.
[6] E. Skamnia, P. Economou, and S. Bersimis, "Detect outliers, breakpoints and level shifts in short financial time series," Communications in Statistics: Case Studies, Data Analysis and Applications, vol. 10, no. 1, pp. 94–105, 2024, doi: 10.1080/23737484.2023.2293691.
[7] C. Xiao, Z. Gou, W. Tai, K. Zhang, and F. Zhou, "Imputation-based time-series anomaly detection with conditional weight-incremental diffusion models," in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '23), Long Beach, CA, USA, Aug. 2023, pp. 2742–2751, doi: 10.1145/3580305.3599391.
[8] C. Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, 2nd ed. Munich: Leanpub, 2022. [Online]. Available: https://christophm.github.io/interpretable-ml-book/.
[9] O. Al-Amary, "AI-driven Early Warning Systems for real estate bubbles," Discover Artificial Intelligence, vol. 6, no. 198, pp. 1–24, Feb. 2026, doi: 10.1007/s44163-026-00899-9.
[10] L. Shang, S. Dermisi, Y. Choe, H. W. Lee, and Y. Min, "Assessing office building marketability before and after the implementation of energy benchmarking and disclosure policies: Lessons learned from major U.S. cities," Sustainability, vol. 15, no. 11, p. 8883, May 2023, doi: 10.3390/su15118883.
[11] D. K. Huriso, B. B. Anjullo, and D. A. Teni, "Modeling the long-run and short-run dynamics of economic indicators in Ethiopia using a vector error correction model," Discover Global Society, vol. 4, p. 39, Mar. 2026, doi: 10.1007/s44282-026-00383-w.
[12] M. Letdin, G. S. Sirmans, G. T. Smersh, and T. Zhou, "The role of tenant characteristics in retail cap rate variation," Journal of Real Estate Finance and Economics, vol. 70, no. 3, pp. 429–456, Apr. 2025, doi: 10.1007/s11146-023-09958-9.
[13] P. T. Lin, "The local nature of housing markets: New evidence," Regional Studies, Regional Science, vol. 12, no. 1, pp. 456–471, 2025, doi: 10.1080/21681376.2025.2477267.
[14] D. Park and D. Ryu, "A machine learning-based Early Warning System for the housing and stock markets," IEEE Access, vol. 9, pp. 68131–68142, May 2021, doi: 10.1109/ACCESS.2021.3077962.
[15] E. Phiri and W. Wang, "Time series analysis and structural break detection: A case of Zambia's CPI," International Journal of Economic Policy, vol. 2, no. 1, pp. 33–43, 2022.
[16] J. Białkowski, S. Titman, and G. Twite, "The determinants of office cap rates: The international evidence," Real Estate Economics, vol. 51, pp. 539–572, 2023, doi: 10.1111/1540-6229.12424.
[17] R. S. Santoso, F. K. Sari Dewi, and Kartika, "Konfigurasi model Prophet untuk prediksi harga saham sektor teknologi di Indonesia yang akurat," Jurnal Buana Informatika, vol. 15, no. 1, pp. 50–58, Apr. 2024, doi: 10.24002/jbi.v15i1.8634.
[18] Cushman & Wakefield, "MarketBeat: Jakarta CBD Office, 1Q2024–3Q2025," Quarterly Market Report, Cushman & Wakefield Indonesia, Jakarta, 2024–2025.
[19] Jones Lang LaSalle (JLL), "Asia Pacific Office Property Clock Q3 2024," Research Report, Jones Lang LaSalle Asia Pacific, Singapore, 2024.
[20] Bank Indonesia, "Laporan Kebijakan Moneter Triwulan IV 2024," Bank Indonesia, Jakarta, Jan. 2025. [Online]. Available: https://www.bi.go.id
[21] Badan Pusat Statistik (BPS), "Produk Domestik Bruto Indonesia Triwulanan 2022–2024," BPS, Jakarta, Feb. 2025. [Online]. Available: https://www.bps.go.id
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright of this journal is assigned to Jurnal Buana Informatika as the journal publisher by the knowledge of author, whilst the moral right of the publication belongs to author. Every printed and electronic publications are open access for educational purposes, research, and library. The editorial board is not responsible for copyright violation to the other than them aims mentioned before. The reproduction of any part of this journal (printed or online) will be allowed only with a written permission from Jurnal Buana Informatika.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
