The Analysis of Property Loans Development in Indonesia

The property sector in Indonesia has an essential role in driving the national economy. The bank lending development to the property sector in April 2019 did not show significant growth and stagnant. This study analyzes the growth trend of property loans in Indonesia and estimates the factors that affect the number of housing loans (KPR) and apartment ownership loans (KPA) in Indonesia. The data used in this study are secondary data and time series. The analytical tools used in this research are trend and regression. The results showed that from 2020 to 2025, the property loan growth in Indonesia will still be sluggish. The condition is identified by construction loan which is expected to grow even though the increase is not too significant, real estate loan is still fluctuating, this is because in 2019 there was a decline in real estate loan growth of almost 50 percent from the previous year, and KPA and KPR are estimated to decline even though in nominal terms the number of KPR and KPA increases. The population number variable has a positive and significant effect on the number of KPR and KPA in Indonesia. The more the population, the more the number of KPR and KPA will increase. Meanwhile, the variables of economic growth and inflation in this study did not significantly affect the number of KPR and KPA.


Introduction
The property sector in Indonesia has an essential role in boosting the national economy.
According to Bank Indonesia (BI), achieving a more robust property sector performance is responsible for various authorities, including BI. BI authorities' policies related to the synergized property sector are believed to accelerate improvements in the property sector's performance. The property sector in Indonesia can absorb a large number of workers. This sector also has a multiplier effect and backward linkage, which significantly impacts other sectors-another impact of the property sector on the economy, especially financial products. An increase in property prices will impact the ability to pay. Thus, it is essential to have synergy between related parties to ensure a healthy and robust property

Article history
Received 2021-01-26 Accepted 2021-03-08 The property sector in Indonesia has an essential role in driving the national economy. The bank lending development to the property sector in April 2019 did not show significant growth and stagnant. This study analyzes the growth trend of property loans in Indonesia and estimates the factors that affect the number of housing loans (KPR) and apartment ownership loans (KPA) in Indonesia. The data used in this study are secondary data and time series. The analytical tools used in this research are trend and regression. The results showed that from 2020 to 2025, the property loan growth in Indonesia will still be sluggish. The condition is identified by construction loan which is expected to grow even though the increase is not too significant, real estate loan is still fluctuating, this is because in 2019 there was a decline in real estate loan growth of almost 50 percent from the previous year, and KPA and KPR are estimated to decline even though in nominal terms the number of KPR and KPA increases. The population number variable has a positive and significant effect on the number of KPR and KPA in Indonesia. The more the population, the more the number of KPR and KPA will increase. Meanwhile, the variables of economic growth and inflation in this study did not significantly affect the number of KPR and KPA. 10.12928/optimum.v10i2.15012 sector work development. In addition, the policy constructed by Indonesia Central Bank (BI) regarding loosening or tightening the Loan to Value ratio (LTV) with due regard to the financial cycle is expected to be able to increase the vigilance of banks in disbursing loans, especially housing loans (KPR).
According to Bank Indonesia, property loans are divided into construction loans, real estate loans and housing loans (KPR), and apartment ownership loans (KPA). The development of bank lending to the property sector in April 2019 did not show significant growth and stagnant. This figure increased slightly from the previous month, which recorded an increase of 17.1% annually. Accelerated housing loans drove this growth (KPR) and apartment ownership loans (KPA), and construction loans. Meanwhile, real estate loans recorded a slowdown in growth. KPR and KPA loan growth was 13.8% (YoY) in April 2019, higher than the previous month's growth of 13.2% (YoY). Meanwhile, property loan disbursement in April 2019 reached IDR 480.4 trillion, higher than the previous month's IDR475.5 trillion. This came from the increase in KPR types 22 to 70 in the Aceh and North Sumatra Provinces.
According to Rahayu, Sri, Betharia, Lela, and Rospida (2018), the determinants that affect housing demand are population, per capita income, and selling price. Pranawengrum and Ciptono (2010) emphasize that the factors that affect residential property demand include housing prices, population, loan interest rates, and inflation in the housing sector.
According to Habiby (2013), the factors that significantly influence customers to borrow housing loans (KPR) are interest rates, income, age, education, and housing location.
Meanwhile, Ganthari and Syafri (2018) stated that consumption loan interest rates and income per capita significantly affect mortgage demand. Siravati (2018) stated that loan interest rates and inflation have a negative and significant effect on demand for housing loans and economic growth, and the loan to deposit ratio has a positive and significant effect on housing loans. Sandria, Adnan, and Yuliana (2016) stated that house prices and loan interest rates affect the demand for housing loans. Thus, refer to the background, it is essential to conduct research related to the property loan development in Indonesia and estimate what are the determinants affect property loan especially for Housing loans (KPR) and Apartment Ownership Loans (KTA).

Data Compilation Method
Secondary data is data that is collected to solve the problem at hand. This data can be found quickly. These secondary data sources in this research are literature, articles, journals, and the internet. According to Silalahi (2012: 289), secondary data is data collected secondhand or from other available sources before the research was conducted. Secondary data were obtained from the Indonesia Central Bureau of Statistics (BPS), Bank Indonesia, the Indonesian Financial Services Authority, and Indonesia Real Estate. The data in this study are time-series data.

Measurement and Variable Assessment
There are two hypotheses in this research that are partial test hypothesis and simultaneous test hypothesis. The hypothesis is as follow: 1. Partial test Hypothesis: a. Housing loans and apartment ownership loan interest rates are significantly affect housing loans and apartment ownership loans in Indonesia.

b. Population significantly affects housing loans and apartment ownership loans in
Indonesia.
c. Economic growth significantly affects housing loans and apartment ownership loans in Indonesia.

d. Inflation significantly affects housing loans and apartment ownership loans in
Indonesia.

Simultaneous test hypothesis:
Independent variables significantly affect dependent variables simultaneously.

Analysis Tools
This research utilized descriptive analysis, trend analysis, and multiple linear regressions. Descriptive statistics and trend analysis are utilized to answer the first purpose 10.12928/optimum.v10i2.15012 of this research. Multiple linear regression is utilized for the second purpose of this research.
A trend tends to up or down in the long run, which is obtained from the average change over time. The rate of change can increase or decrease. If the rate of change increases, it is called a positive trend, or the trend has an upward tendency. Conversely, if the average change decreases, it is called a negative trend or a trend that has a downward trend. The trend line is a regression line, and the independent variable (x) is a time variable. A straight line (linear) trend is a trend that is predicted to rise or fall in a straight line. Time variable as an independent variable can use annual, semester, monthly, or weekly time. Analysis of the straight-line trend (linear) consists of small squares or (least square) and moment.
The equation of trend is as follow Y' = a+bX There are several methods of trend analysis. The methods are as follow: 1) Free Hand Method. Drawing a trend with this free method is very easy and straightforward. Only by observing the distribution of data can we know the trend line trend of the data pattern. Of course, in this way, the results cannot be justified.
2) Semi Average Method. Move to the trend line by finding the group means. This method is to try to eliminate subjectivity, as in the free method. Spiegel (2004)  Indonesia's construction loan development trend is projected for the next five years with the ceteris paribus assumption and based on historical data from 2009 to 2025. The construction loan growth from 2010 to 2011 experienced a 16.8% significant increase.
Meanwhile, the construction loan growth experienced fluctuations in the following years.
Construction loan growth is predicted to grow steadily from 2020 to 2025 even though during 2009-2020, there were fluctuations. Figure 1 shows construction loan growth that tends to decline with the loan value increasing every year.
Data Source: Bank Indonesia

The effect of population, economic growth, and inflation on housing loan and apartment ownership loan in Indonesia.
The classical assumption is conducted before running multiple linear regressions. After the data meets the classical assumption test, new data can be processed with multiple linear regression. Four types of classical assumption tests are autocorrelation, normalization, multicollinearity, and heteroscedasticity.      Table 4, it can be seen that the Durbin Watson value is 0.670, the du value is 1.8640, and the dl value is 0.6577. Those values do not explain definite conclusions because the Durbin Watson value is between the dl and du values. After passed the classical assumption test, the existing data can be further estimated using multiple linear regression analysis tools. Below are the results of multiple linear regressions.

Discussion
This study revealed that the number of populations (JP) has a positive effect on housing loans (KPR) and apartment ownership loans (KTA). It means that the higher the population.
the higher the demand for KPR and KTA. This is in line with Pranawengrum and Ciptono (2010 which stated that the number of residents has a significant and positive impact on residential property demand. In addition. it is also supported by the research of Rahayu. Betharia. Lela. and Rospida (2009). The research revealed that the population has a positive and significant effect on housing demand in Bengkulu.

Suggestion
The government needs to construct some monetary policies to boost property loan growth in Indonesia. Furthermore. the increasing population will increase demand for housing. both housing and apartments. It stimulates the regional government to carry out regulatory policies related to the housing and apartment development in their respective areas in terms of fulfilling housing for residents. In addition. the central government can intensify housing or apartment ownership loans for the lower class. for example. with housing subsidies or a special reduction in interest rates for underprivileged residents.
These policies should be done to avoid further problems related to housing as a primary need.