1# RECORD 74099967a079fff42f0df3135c0f69cc4e0596152# TITLE: A Quantitative Examination of Housing Price Determinants in Yogyakarta3# YEAR: 2025.0 VENUE: Determinasi: Jurnal Penelitian Ekonomi Manajemen dan Akuntansi DOI: 10.23917/determinasi.v3i4.5024# AUTHORS: Bagas Yudhanto Aji; Elit Rizki Wijaya; Juliana Diva Putri Raharjo; Nur Fazillah; Nuke Allya Nurmadina5# PAGES: 11 CANDIDATE TABLE PAGES (1-based): [6]6# ABSTRACT: This study examines the determinants of housing prices in Yogyakarta, focusing on structural property features such as land area, bedrooms, bathrooms, vehicle slots, and house area. Utilizing a quantitative approach with multiple linear regression on secondary data, the research identifies land area as the most significant predictor of property value, followed by bedrooms and bathrooms. Vehicle slots provide moderate influence, while house area shows a lesser impact. These findings highlight the critical role of land scarcity and urban growth in shaping housing prices, offering actionable insights for policymakers, urban planners, and developers. Future studies should explore qualitative factors like neighborhood quality and cultural influences to complement these results.789===== PAGE 1 (score 10, numeric-paren 0, table-words 0, var-words 10) =====1011 12Determinasi: Jurnal Ekonomi Manajemen dan Akuntansi 13Vol. 3, No. 4, Tahun 2025 14page. 452 – 462 15e-ISSN: 2986 – 2027 (online) 16 17https://jsr.lib.ums.ac.id/index.php/determinasi 18▪ page 452 19 20Received October 5, 2025; Revised October 30, 2025; Accepted November 15, 2025 21A Quantitative Examination of Housing Price Determinants in 22Yogyakarta 23 24Bagas Yudhanto Aji1, Elit Rizki Wijaya2, Juliana Diva Putri Raharjo3, Nurul Fazillah4, Nuke Allya 25Nurmadina5 261Universitas Muhammadiyah Surakarta 27Jl. A. Yani, Mendungan, Pabelan, Kartasura, Sukoharjo 57169, (0271) 717417, e-mail: 28b100224053@student.ums.ac.id 292Universitas Muhammadiyah Surakarta 30Jl. A. Yani, Mendungan, Pabelan, Kartasura, Sukoharjo 57169, (0271) 717417, e-mail: 31b100224233@student.ums.ac.id 323Universitas Muhammadiyah Surakarta 33Jl. A. Yani, Mendungan, Pabelan, Kartasura, Sukoharjo 57169, (0271) 717417, e-mail: 34b100224195@student.ums.ac.id 354Universitas Muhammadiyah Surakarta 36Jl. A. Yani, Mendungan, Pabelan, Kartasura, Sukoharjo 57169, (0271) 717417, e-mail: 37b100224625@student.ums.ac.id 385Universitas Muhammadiyah Surakarta 39Jl. A. Yani, Mendungan, Pabelan, Kartasura, Sukoharjo 57169, (0271) 717417, e-mail: 40b100224057@student.ums.ac.id 41ARTICLE INFO 42 43Article history: 44 45Received 5 Oktober 2025 46Received in revised form 30 Oktober 2025 47Accepted 15 November 2025 48Available online 5 Desember 2025 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67ABSTRACT 68 69This study examines the determinants of housing prices 70in Yogyakarta, focusing on structural property features 71such as land area, bedrooms, bathrooms, vehicle slots, 72and house area. Utilizing a quantitative approach with 73multiple linear regression on secondary data, the 74research identifies land area as the most significant 75predictor of property value, followed by bedrooms and 76bathrooms. Vehicle slots provide moderate influence, 77while house area shows a lesser impact. These findings 78highlight the critical role of land scarcity and urban 79growth in shaping housing prices, offering actionable 80insights for policymakers, urban planners, and 81developers. Future studies should explore qualitative 82factors like neighborhood quality and cultural 83influences to complement these results. 84 85Keywords: Housing prices, property features, land 86area, urban growth, quantitative analysis.87 88Abstrak 89Penelitian ini mengkaji faktor-faktor penentu harga perumahan di Yogyakarta dengan fokus pada 90karakteristik struktural properti seperti luas tanah, jumlah kamar tidur, jumlah kamar mandi, ketersediaan 91tempat parkir kendaraan, dan luas bangunan. Dengan menggunakan pendekatan kuantitatif melalui regresi 92linier berganda terhadap data sekunder, penelitian ini mengidentifikasi bahwa luas tanah merupakan prediktor 93paling signifikan terhadap nilai properti, diikuti oleh jumlah kamar tidur dan kamar mandi. Ketersediaan 94tempat parkir kendaraan memberikan pengaruh sedang, sementara luas bangunan menunjukkan dampak yang 95lebih kecil. Temuan ini menyoroti peran penting kelangkaan lahan dan pertumbuhan perkotaan dalam 96membentuk harga perumahan, serta memberikan wawasan praktis bagi pembuat kebijakan, perencana kota, 97dan pengembang. Penelitian selanjutnya disarankan untuk mengeksplorasi faktor kualitatif seperti kualitas 98lingkungan dan pengaruh budaya guna melengkapi hasil penelitian ini. 99Kata Kunci: Harga perumahan, karakteristik properti, luas tanah, pertumbuhan perkotaan, analisis 100kuantitatif. 101102103===== PAGE 6 (score 15, numeric-paren 0, table-words 2, var-words 8) =====104105 106457 107 108e-ISSN : 2986-2027 109 110Determinasi: Jurnal Ekonomi Manajemen dan Akuntansi 111Vol. 3, No. 4, Tahun 2025 112page. 452 – 462 113Inflation Factors (VIF). Unidimensional reliability analysis was conducted using the ω (omega) coefficient 114to evaluate the internal consistency and reliability of the data. The combination of descriptive statistics, 115multiple linear regression, and reliability analysis offers a comprehensive framework for understanding how 116property characteristics affect housing prices. Sugiyono (2018) highlights that regression analysis is an 117appropriate method for modelling the relationship between multiple predictors and dependent variables, 118allowing for robust predictive insights and statistical interpretation. This methodological approach allows for 119a detailed and data-driven exploration of the Yogyakarta housing market, supporting informed urban planning 120and policy recommendations. 121 122 123Figure 1. Research Model 124 1254. 126RESULT AND DISCUSSION 1274.1. 128Descriptive Statistics 129The descriptive statistics analysis highlights the key characteristics of the dataset used in this research. The 130average housing price in Yogyakarta was IDR 1.840 billion (SD = IDR 2.700 billion), with prices ranging 131from IDR 7 million to IDR 42.1 billion. The mean land area was 185.71 m² (SD = 250.70 m²), while the 132average house area was 1,193 m² (SD = 5.36×10⁷ m²), showing significant variability in property sizes. 133Structural features such as the number of bedrooms (mean = 3.97, SD = 3.67), bathrooms (mean = 2.94, SD 134= 3.52), and vehicle slots (mean = 1.56, SD = 1.28) also exhibited moderate variation, reflecting the diversity 135of properties included in the dataset. 136 137Table 1. Descriptive Statistics 138 139Y 140X1 141X2 142X3 143X4 144X5 145Valid 1462020 1472001 1481999 1492019 1502019 1511713 152Missing 1530 15419 15521 1561 1571 158307 159Mean 1601.840×10+9 1613.969 1622.942 163185.711 1641.193×10+61651.559 166Std. Deviation 1672.700×10+9 1683.672 1693.518 170250.697 1715.359×10+71721.283 173Minimum 1747.000×10+6 1751.000 1761.000 1774.000 1784.000 1791.000 180Maximum 1814.210×10+1018249.000 18349.0001844222.0001852.408×10+918615.000187 188 189 190 191192193===== PAGE 7 (score 20, numeric-paren 0, table-words 7, var-words 0) =====194195 196458 197 198e-ISSN : 2986-2027 199 200Determinasi: Jurnal Ekonomi Manajemen dan Akuntansi 201Vol. 3, No. 4, Tahun 2025 202page. 452 – 462 203 204 205 2064.2. 207Linear Regression Analysis 208Linear regression analysis assessed the relationship between housing price (dependent variable) and property 209attributes (independent variables). The model's R² value was 0.428, indicating that the predictors accounted 210for 42.8% of the variance in housing prices. The model achieved statistical significance: F(5, 1707) = 211255.836, p < 0.001. 212 213Table 2. Model Summary – Price (Y) 214Model 215R 216R² 217Adjusted R² 218RMSE 219M₀ 2200.0002210.0002220.000 2232.296×10+9224M₁ 2250.6542260.4282270.427 2281.738×10+9229Note. M₁ includes X1, X2, X3, X4, X5 230 231To further examine the significance of the predictors, an ANOVA test confirmed the model's significance (p 232< 0.001), as shown in Table 3. 233 234Table 3. ANOVA 235Model 236 237Sum of Squares 238df 239Mean Square 240F 241p 242M₁ 243Regression2443.865×10+21 2455 2467.730×10+20 247255.836248< .001249 250Residual 2515.158×10+21 25217072533.022×10+18 254 255 256 257Total 2589.023×10+21 2591712260 261 262 263Note. M₁ includes X1, X2, X3, X4, X5 264Note. The intercept model is omitted, as no meaningful information can be shown. 265 266The regression coefficients in Table 4 highlight the relative contribution of each variable to housing price. 267 268Table 4. Coefficients 269 270Collinearity 271Statistics 272Mo273del 274 275Unstandar276dized 277Standard 278Error 279Stand280ardize281d 282t 283p 284Toler285ance 286VIF 287M₀ 288(Intercept) 2891.685×10+9 2905.547×10+7 291 29230.385 293< .001 294 295 296M₁ 297(Intercept) 298-1.120×10+7 2998.006×10+7 300 301-0.140 3020.889 303 304 305 306X1 3078.854×10+7 3083.939×10+7 3090.131 3102.248 3110.025 3120.099 31310.079 314 315X2 3161.642×10+8 3173.939×10+7 3180.244 3194.169 320< .001 3210.097 32210.257 323 324X3 3253.729×10+6 326243986.024 3270.359 32815.282 329< .001 3300.608 3311.643 332 333X4 3340.301 3350.722 3360.008 3370.417 3380.677 3391.000 3401.000 341 342X5 3431.606×10+8 3444.408×10+7 3450.090 3463.643 347< .001 3480.552 3491.812 350 3514.3. 352Unidemensional Reliability Analysis 353 354Reliability analysis using the ω coefficient yielded an estimate of 0.00037 (95% CI: 3.92×10⁻⁷ to 0.001), 355indicating low internal consistency. The X5 (vehicle slots) variable negatively correlated with the scale, 356which may have contributed to the reduced reliability. 357