# RECORD 74099967a079fff42f0df3135c0f69cc4e059615
# TITLE: A Quantitative Examination of Housing Price Determinants in Yogyakarta
# YEAR: 2025.0  VENUE: Determinasi: Jurnal Penelitian Ekonomi Manajemen dan Akuntansi  DOI: 10.23917/determinasi.v3i4.502
# AUTHORS: Bagas Yudhanto Aji; Elit Rizki Wijaya; Juliana Diva Putri Raharjo; Nur Fazillah; Nuke Allya Nurmadina
# PAGES: 11  CANDIDATE TABLE PAGES (1-based): [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.


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Determinasi: Jurnal Ekonomi Manajemen dan Akuntansi 
Vol. 3, No. 4, Tahun 2025 
page. 452 – 462  
e-ISSN: 2986 – 2027 (online) 
 
https://jsr.lib.ums.ac.id/index.php/determinasi  
▪ page 452 
 
Received October 5, 2025; Revised October 30, 2025; Accepted November 15, 2025 
A Quantitative Examination of Housing Price Determinants in 
Yogyakarta 
 
Bagas Yudhanto Aji1, Elit Rizki Wijaya2, Juliana Diva Putri Raharjo3, Nurul Fazillah4, Nuke Allya 
Nurmadina5 
1Universitas Muhammadiyah Surakarta 
Jl. A. Yani, Mendungan, Pabelan, Kartasura, Sukoharjo 57169, (0271) 717417, e-mail: 
b100224053@student.ums.ac.id 
2Universitas Muhammadiyah Surakarta 
Jl. A. Yani, Mendungan, Pabelan, Kartasura, Sukoharjo 57169, (0271) 717417, e-mail: 
b100224233@student.ums.ac.id  
3Universitas Muhammadiyah Surakarta 
Jl. A. Yani, Mendungan, Pabelan, Kartasura, Sukoharjo 57169, (0271) 717417, e-mail: 
b100224195@student.ums.ac.id 
4Universitas Muhammadiyah Surakarta 
Jl. A. Yani, Mendungan, Pabelan, Kartasura, Sukoharjo 57169, (0271) 717417, e-mail: 
b100224625@student.ums.ac.id 
5Universitas Muhammadiyah Surakarta 
Jl. A. Yani, Mendungan, Pabelan, Kartasura, Sukoharjo 57169, (0271) 717417, e-mail: 
b100224057@student.ums.ac.id 
ARTICLE  INFO 
 
Article history:  
 
Received 5 Oktober 2025 
Received in revised form 30 Oktober 2025 
Accepted 15 November 2025 
Available online 5 Desember 2025 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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. 
 
Keywords: Housing prices, property features, land 
area, urban growth, quantitative analysis.
 
Abstrak 
Penelitian ini mengkaji faktor-faktor penentu harga perumahan di Yogyakarta dengan fokus pada 
karakteristik struktural properti seperti luas tanah, jumlah kamar tidur, jumlah kamar mandi, ketersediaan 
tempat parkir kendaraan, dan luas bangunan. Dengan menggunakan pendekatan kuantitatif melalui regresi 
linier berganda terhadap data sekunder, penelitian ini mengidentifikasi bahwa luas tanah merupakan prediktor 
paling signifikan terhadap nilai properti, diikuti oleh jumlah kamar tidur dan kamar mandi. Ketersediaan 
tempat parkir kendaraan memberikan pengaruh sedang, sementara luas bangunan menunjukkan dampak yang 
lebih kecil. Temuan ini menyoroti peran penting kelangkaan lahan dan pertumbuhan perkotaan dalam 
membentuk harga perumahan, serta memberikan wawasan praktis bagi pembuat kebijakan, perencana kota, 
dan pengembang. Penelitian selanjutnya disarankan untuk mengeksplorasi faktor kualitatif seperti kualitas 
lingkungan dan pengaruh budaya guna melengkapi hasil penelitian ini. 
Kata Kunci: Harga perumahan, karakteristik properti, luas tanah, pertumbuhan perkotaan, analisis 
kuantitatif. 


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457 
 
e-ISSN :  2986-2027 
 
Determinasi: Jurnal Ekonomi Manajemen dan Akuntansi  
Vol. 3, No. 4, Tahun 2025  
page. 452 – 462 
Inflation Factors (VIF). Unidimensional reliability analysis was conducted using the ω (omega) coefficient 
to evaluate the internal consistency and reliability of the data. The combination of descriptive statistics, 
multiple linear regression, and reliability analysis offers a comprehensive framework for understanding how 
property characteristics affect housing prices. Sugiyono (2018) highlights that regression analysis is an 
appropriate method for modelling the relationship between multiple predictors and dependent variables, 
allowing for robust predictive insights and statistical interpretation. This methodological approach allows for 
a detailed and data-driven exploration of the Yogyakarta housing market, supporting informed urban planning 
and policy recommendations. 
 
 
Figure 1. Research Model 
 
4. 
RESULT AND DISCUSSION 
4.1. 
Descriptive Statistics 
The descriptive statistics analysis highlights the key characteristics of the dataset used in this research. The 
average housing price in Yogyakarta was IDR 1.840 billion (SD = IDR 2.700 billion), with prices ranging 
from IDR 7 million to IDR 42.1 billion. The mean land area was 185.71 m² (SD = 250.70 m²), while the 
average house area was 1,193 m² (SD = 5.36×10⁷ m²), showing significant variability in property sizes. 
Structural features such as the number of bedrooms (mean = 3.97, SD = 3.67), bathrooms (mean = 2.94, SD 
= 3.52), and vehicle slots (mean = 1.56, SD = 1.28) also exhibited moderate variation, reflecting the diversity 
of properties included in the dataset. 
 
Table 1. Descriptive Statistics  
  
Y 
X1 
X2 
X3 
X4 
X5 
Valid 
2020 
2001 
1999 
2019 
2019 
1713 
Missing 
0 
19 
21 
1 
1 
307 
Mean 
1.840×10+9  
3.969 
2.942 
185.711 
1.193×10+6
1.559 
Std. Deviation 
2.700×10+9  
3.672 
3.518 
250.697 
5.359×10+7
1.283 
Minimum 
7.000×10+6  
1.000 
1.000 
4.000 
4.000 
1.000 
Maximum 
4.210×10+10
49.000 
49.000
4222.000
2.408×10+9
15.000
 
 
 
 


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458 
 
e-ISSN :  2986-2027 
 
Determinasi: Jurnal Ekonomi Manajemen dan Akuntansi  
Vol. 3, No. 4, Tahun 2025  
page. 452 – 462 
 
 
 
4.2. 
Linear Regression Analysis 
Linear regression analysis assessed the relationship between housing price (dependent variable) and property 
attributes (independent variables). The model's R² value was 0.428, indicating that the predictors accounted 
for 42.8% of the variance in housing prices. The model achieved statistical significance: F(5, 1707) = 
255.836, p < 0.001. 
 
Table 2. Model Summary – Price  (Y)  
Model 
R 
R² 
Adjusted R² 
RMSE 
M₀ 
0.000
0.000
0.000 
2.296×10+9
M₁ 
0.654
0.428
0.427 
1.738×10+9
Note.  M₁ includes X1, X2, X3, X4, X5 
 
To further examine the significance of the predictors, an ANOVA test confirmed the model's significance (p 
< 0.001), as shown in Table 3. 
 
Table 3. ANOVA  
Model 
  
Sum of Squares 
df 
Mean Square 
F 
p 
M₁ 
Regression
3.865×10+21  
5 
7.730×10+20  
255.836
< .001
  
Residual 
5.158×10+21  
1707
3.022×10+18  
 
  
  
Total 
9.023×10+21  
1712
 
 
  
Note.  M₁ includes X1, X2, X3, X4, X5 
Note.  The intercept model is omitted, as no meaningful information can be shown. 
 
The regression coefficients in Table 4 highlight the relative contribution of each variable to housing price. 
 
Table 4. Coefficients  
 
Collinearity 
Statistics 
Mo
del 
  
Unstandar
dized 
Standard 
Error 
Stand
ardize
d 
t 
p 
Toler
ance 
VIF 
M₀ 
(Intercept) 
1.685×10+9  
5.547×10+7  
 
30.385 
< .001 
  
 
M₁ 
(Intercept) 
-1.120×10+7 
8.006×10+7  
 
-0.140 
0.889 
  
 
  
X1 
8.854×10+7  
3.939×10+7  
0.131 
2.248 
0.025 
0.099 
10.079 
  
X2 
1.642×10+8  
3.939×10+7  
0.244 
4.169 
< .001 
0.097 
10.257 
  
X3 
3.729×10+6  
243986.024 
0.359 
15.282 
< .001 
0.608 
1.643 
  
X4 
0.301 
0.722 
0.008 
0.417 
0.677 
1.000 
1.000 
  
X5 
1.606×10+8  
4.408×10+7  
0.090 
3.643 
< .001 
0.552 
1.812 
 
4.3. 
Unidemensional Reliability Analysis 
 
Reliability analysis using the ω coefficient yielded an estimate of 0.00037 (95% CI: 3.92×10⁻⁷ to 0.001), 
indicating low internal consistency. The X5 (vehicle slots) variable negatively correlated with the scale, 
which may have contributed to the reduced reliability.