Geçinme Endeksi Değerinin Araştırılması ve Tahminlenmesi: Türkiye Geneli ve İstanbul Özeli Örneği

Abstract

Geçinme endeksi, hane halklarının mevcut yaşam standartlarını sürdürebilmeleri için gereken harcama düzeyini gösteren ve toplumun alım gücünü doğrudan yansıtan temel bir ekonomik göstergedir. Bu endeksteki değişimlerin izlenmesi, hem uygulanan ekonomi politikalarının etkinliğini ölçmek hem de toplumsal refahın korunmasına yönelik stratejiler geliştirmek açısından kritik bir önem taşımaktadır. Bu çalışmada, Türkiye geneli ve İstanbul özelindeki geçinme endeksi değerlerini analiz etmek amacıyla kapsamlı bir veri setinden yararlanılmıştır. Araştırmanın veri seti, Türkiye Cumhuriyet Merkez Bankası (TCMB) ve Türkiye İstatistik Kurumu (TÜİK) verilerinden derlenen Ağustos 2013 ile Aralık 2025 dönemine ait 149 aylık süreci kapsamaktadır. Analizlerde algoritma olarak, tahmin başarısını belirlemek amacıyla Bagging topluluk öğrenmesi ile Değişken Model Ağacı (AMT) algoritmasının hibrit bir yapısı kullanılmaktadır. Ayrıca, endeks üzerinde etkili olan değişkenleri belirlemek için ReliefF öznitelik seçimi algoritmasından yararlanılmaktadır. Elde edilen sonuçlar, algoritmanın hem ulusal hem de bölgesel ölçekte yüksek bir tahmin başarısı sergilediğini göstermektedir. Özellikle 2026 yılına yönelik yapılan 12 aylık tahminlerde, geçinme endeksinin her iki bölgede de paralel ancak İstanbul’da daha keskin bir artış eğilimi içerisinde olduğu görülmektedir. Değişkenlerin önem sıralamasına bakıldığında ise konut harcamalarının hem Türkiye genelinde hem de İstanbul’da geçinme maliyetlerini tetikleyen en temel unsur olduğu belirlenmektedir. İstanbul özelinde ulaştırma maliyetlerinin Türkiye geneline kıyasla daha baskın bir ağırlığa sahip olması, bölgesel dinamiklerin farklılaştığını ortaya koymaktadır.

Keywords

• Geçinme endeksi
• bagging
• değişken model ağacı
• reliefF
• tahminleme.

Supporting Institution

Çalışmada herhangi bir kurum ya da kuruluştan destek alınmamıştır.

Ethical Statement

Etik kurul belgesi gerektiren bir çalışma değildir.

Thanks

Çalışmada herhangi bir kurum ya da kuruluştan destek alınmamıştır.

Investigation and Forecasting of The Cost of Living Index: The Case of Türkiye and İstanbul

Abstract

The cost-of-living index represents the spending level required for households to maintain their current living standards. It directly reflects the purchasing power of society. Monitoring changes in this index is critical for measuring the effectiveness of economic policies. It also helps develop strategies to protect social welfare. This study uses a comprehensive dataset to analyze cost of living index values for Türkiye and Istanbul. The dataset covers 149 months between August 2013 and December 2025. These data come from the Central Bank of the Republic of Türkiye (CBRT) and the Turkish Statistical Institute (TURKSTAT). The analysis employs a hybrid structure of Bagging ensemble learning and the Alternating Model Tree (AMT) algorithm. This approach determines the forecasting success. Additionally, the ReliefF feature selection algorithm identifies the effective variables the index. Results show high forecasting success at both national and regional levels. The 12-month forecasts for 2026 indicate a parallel upward trend in both regions. However, Istanbul shows a sharper increase. Housing expenses emerge as the primary factor driving living costs in both Türkiye and Istanbul. Transportation costs hold more weight in Istanbul compared to the national average. This difference highlights varying regional dynamics.

Keywords

• The cost-of-living index
• bagging
• alternating model tree
• reliefF
• forecasting

Supporting Institution

No support is taken from any institution or organization.

Ethical Statement

The study does not necessitate an approval of ethical committee.

Thanks

No support is taken from any institution or organization.

References

1. Athey, S. (2018). The impact of machine learning on economics. In The economics of artificial intelligence: An agenda (pp. 507-547). University of Chicago Press. https://doi.org/10.1257/aer.p20151023
2. Attinasi, M. G., Balatti, M., Mancini, M., & Metelli, L. (2022). Supply chain disruptions and the effects on the global economy. Economic Bulletin Boxes, 8.
3. Auer, R., Borio, C. E., & Filardo, A. J. (2017). The globalisation of inflation: the growing importance of global value chains. (March 2017). CEPR Discussion Paper No. DP11905. https://ssrn.com/abstract=2937754
4. Bahadir, B., & Gumus, I. (2022). House prices, collateral effects and sectoral output dynamics in emerging market economies. Journal of International Money and Finance, 129, 102724. https://doi.org/10.1016/j.jimonfin.2022.102724
5. Baycan, İ. O., & Yıldırım, G. (2016). Analyzing the nonlinear dynamics of the turkish textile and apparel industries. Textile and Apparel, 26(4), 345-350. https://izlik.org/JA83EW94PX
6. Beatty, T. K., & Larsen, E. R. (2005). Using Engel curves to estimate bias in the Canadian CPI as a cost of living index. Canadian Journal of Economics/Revue canadienne d'économique, 38(2), 482-499. https://doi.org/10.1111/j.0008-4085.2005.00289.x
7. Berry, W. D., Fording, R. C., & Hanson, R. L. (2000). An annual cost of living index for the American states, 1960‐1995. Journal of Politics, 62(2), 550-567. https://doi.org/10.1111/0022-3816.00025
8. Boskin, M. J. (2005). Causes and consequences of bias in the consumer price ındex as a measure of the cost of living. Atlantic Economic Journal, 33(1), 1-13. https://doi.org/10.1007/s11293-005-1631-6

9. Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-140. https://doi.org/10.1007/BF00058655
10. Canitez, F. (2025). Transport Justice for Istanbul’s Urban Mobility: Equity, Accessibility, and Policy Challenges. Journal of Operations Intelligence, 3(1), 232-244. https://doi.org/10.31181/jopi31202544
11. Central Bank of the Republic of Türkiye Web Page. (2026, 15 March). The Cost-of-Living Index. Central Bank of the Republic of Türkiye. https://evds2.tcmb.gov.tr/index.php?/evds/serieMarket/#collapse_2.
12. Çam, A. V. (2013). Application of a new ındex for stakeholders: student living ındex. The Journal of Social and Economic Research, 13(25), 127-144. https://izlik.org/JA99RE83NK
13. Çelikok, K., & Talih, Ö. (2023). International transportation projects and Türkiye from the perspective of transportation economics. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 24(2), 400-423. https://doi.org/10.17494/ogusbd.1298169
14. Diewert, W. E. (1990). The theory of the cost-of-living index and the measurement of welfare change. Contributions to Economic Analysis, 196, 79-147. https://doi.org/10.1016/B978-0-444-88108-3.50007-X
15. Duğru, M. (2025). The effect of consumer subsistence index, subsistence index, industrial production index on debit card and credit card expenditures in Türkiye: 2015-2023. Marmara University Journal of Economic & Administrative Sciences, 47(3), 238-256. https://doi.org/10.14780/muiibd.1550830
16. Filiz, E. (2023). Evaluation of match results of five successful football clubs with ensemble learning algorithms. Research quarterly for exercise and sport, 94(3), 773-782. https://doi.org/10.1080/02701367.2022.2053647
17. Filiz, E., & Öz, E. (2019). Finding the best algorithms and effective factors in classification of Turkish science student success. Journal of Baltic Science Education, 18(2), 239-253. https://doi.org/10.33225/jbse/19.18.239
18. Forte-Campos, V., Moral-Benito, E., & Quintana, J. (2021). A cost of living index for Spanish cities. Banco de Espana Article, 29, 21. https://ssrn.com/abstract=3918728
19. Frank, E., Mayo, M., & Kramer, S. (2015, April). Alternating model trees. In Proceedings of the 30th annual ACM symposium on applied computing, 871-878. https://doi.org/10.1145/2695664.2695848
20. Keskin, Ö. (2025). Analysis of causality between the cost of living index and average individual credit card risk per capita. Journal of Abant Social Sciences, 25(1), 188-200. https://doi.org/10.11616/asbi.1585781
21. Kira, K., & Rendell, L. A. (1992). A practical approach to feature selection. In Machine learning proceedings 1992, 249-256. Morgan Kaufmann. https://doi.org/10.1016/B978-1-55860-247-2.50037-1
22. Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015). Prediction policy problems. American Economic Review, 105(5), 491-495.
23. Klimek, S. (2024). Public–private partnership (PPP) and ICT in a mega-smart city: The case of Istanbul. In Smart Cities (pp. 187-201). Routledge. https://doi.org/10.1201/9781003415930
24. National Research Council. (2002). At what price?: conceptualizing and measuring cost-of-living and price indexes. National Research Council. 2002. At What Price?: Conceptualizing and Measuring Cost-of-Living and Price Indexes. Washington, 10131.
25. Ngo, D. K. (2018). A theory-based living standards index for measuring poverty in developing countries. Journal of Development Economics, 130, 190-202. https://doi.org/10.1016/j.jdeveco.2017.10.011
26. Osborne, M. (2018). Approximating the cost-of-living index for a storable good. American Economic Journal: Microeconomics, 10(2), 286-314. https://doi.org/10.1257/mic.20140254
27. Panat, T., & Chandra, R. (2025). Global ease of living index: a machine learning framework for longitudinal analysis of major economies. arXiv preprint, arXiv:2502.06866. https://doi.org/10.48550/arXiv.2502.06866
28. Paredes Araya, D., & Iturra Rivera, V. (2013). Substitution bias and the construction of a spatial cost of living index. Papers in Regional Science, 92(1), 103-117. https://doi.org/10.1111/j.1435-5957.2011.00408.x
29. Robnik-Šikonja, M., & Kononenko, I. (1997, July). An adaptation of Relief for attribute estimation in regression. In Machine learning: Proceedings of the fourteenth international conference (ICML’97), 5, 296-304.
30. Sarangi, P. K., Sahoo, A. K., & Sinha, S. (2022, February). Modeling consumer price index: a machine learning approach. In macromolecular symposia (Vol. 401, No. 1, p. 2100349). https://doi.org/10.1002/masy.202100349
31. Searle, R. (2015). Is there anything real about real wages? A history of the official British cost of living index, 1914–62. The Economic History Review, 68(1), 145-166. https://doi.org/10.1111/1468-0289.12059
32. Sönmezler, G., Gündüz, İ. O., & Torun, M. (2019). An empirical study on the relationship between credit card expenditures, consumer confidence index and inflation in Turkey. Journal of Çukurova University Institute of Social Sciences, 28(1), 17-29.
33. Şahinler, A. N., Yılmazer, D., & Kaplaner, S. (2025). The Impact of Inflation and Exchange Rate on Housing Prices: Evidence from Bootstrap ARDL with Sharp and Smooth Structural Breaks. Industrial Policy, 5(Special Issue 1), 129-137. https://doi.org/10.61192/indpol.1822101
34. Triplett, J. E. (2001). Should the cost‐of‐living index provide the conceptual framework for a consumer price index?. The Economic Journal, 111(472), 311-334. https://doi.org/10.1111/1468-0297.00633
35. Tunc, C., & Gunes, A. (2023). Monetary policy and house prices in emerging markets. International Journal of Housing Markets and Analysis, 16(5), 873-891. https://doi.org/10.1108/IJHMA-03-2022-0041
36. Turkish Statistical Institute (TurkStat) Web Page. (2026, 15 March), Turkish Statistical Institute. https://data.tuik.gov.tr/.
37. Ueda, K., Watanabe, K., & Watanabe, T. (2019). Product turnover and the cost-of-living index: Quality versus fashion effects. American Economic Journal: Macroeconomics, 11(2), 310-347. https://doi.org/10.1257/mac.20160403
38. Ünal, A. E., & Caner, A. A. (2025). Household living costs in the process of digital financialization: evidence from fourier and TodaYamamoto analyses. Journal of Social Sciences Eskişehir Osmangazi University, 26(special issue), 2224-2242. https://doi.org/10.17494/ogusbd.1762770
39. Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of economic perspectives, 28(2), 3-28. https://doi.org/10.1257/jep.28.2.3