Estate Policy Research Articles

통화정책 불확실성이 토지가격에 미치는 영향에 관한 연구

This study empirically analyzed whether the impact of MPU(Monetary Policy Uncertainty) Index have a significant effect on Land prices using the MRSAR(Markov Regime Switching Autoregressive) model. The result of the study is as follows. We find that the impact of MPU index significantly affect on the land market in each regime. First, it is found that the impact of uncertainty could be spillover internationally. Second, it is found that MPU index in Korea and Japan affect the land market in a similar way. However, the MPU index in the U.S. is found to affect the land market only during the first half of the financial crisis. Third, it is found that the expected duration of MPU shocks in Korea, the United States, and Japan lasted about seven times more during the recession than during the boom. This provides a surprising insight that the impact of uncertainty can be spillover internationally. Therefore, in order to improve the effectiveness and predictive power of real estate policies, the need for model development including MPU indicators is required.

IMPLEMENTASI FOOD ESTATE TERHADAP TINGKAT PARTISIPASI ANGKATAN KERJA DI INDONESIA

Food estate policies implemented by the government in several provinces are aimed at improving the welfare of farmers and increasing the production of agricultural products. an increase in farmers' welfare is indicated by an increase in the farmer's exchange rate (NTP) and an increase in the production of agricultural products as indicated by the growth of GRDP in the agricultural sector. This study aims to determine how much influence the implementation of food estate has on employment in the agricultural sector (TPAK). This study was analyzed by panel data regression and the selected model is the Fixed Effect Model. The results showed that the GRDP growth variable in the agricultural sector had a positive and significant effect on TPAK. Meanwhile, the NTP variable has a negative and insignificant effect on TPAK in the provinces where the food estate policy is applied

Comparative analysis of President Susilo Bambang Yudhoyono and Joko Widodo’s food estate policies as a national food securitization attempts

Food-related issues affecting many people have long been a point of concern for nations, including Indonesia. President Susilo Bambang Yudhoyono (SBY) and Joko Widodo (Jokowi) had signed policies for food resilient policies that were riddled with problems such as land rights misallocation. Therefore, this research is conducted to answer research questions, including the comparison of food estate policies during the SBY and Jokowi, the act of securitization by both governments using food estate policies, and the inclusion of local farmers or people’s interest in the implementation of food estate policy strategies. The descriptive approach explains the impact and insufficiencies of policy implementation in their respective regimes. The comparison technique is called Qualitative Comparative Analysis (QCA), focusing on seeking the most similar out of the most different. The securitization concept is used to define a wide-ranging strategy utilized to reduce food vulnerabilities. This research showcased several differences between each regime’s national food securitization attempts, especially regarding the involvement of military mechanisms in Jokowi’s food estate. This research proved the existence of securitization attempts in each regime’s policy implementation, with problems relating to land access and lack of local farmers’ agencies. Researchers found that this is partially caused by limited government understanding surrounding food security, which only persisted on availability instead of sustainability.

Spatiotemporal Evolution and Cause Analysis of Urban Housing Investment Resilience: An Empirical Study of 35 Large and Medium-Sized Cities in China

In this study, we explore the evolution and formation mechanism of urban housing investment resilience from three perspectives: theoretical analysis, model construction, and empirical testing. Based on the three-element theory of investment decision making and urban resilience system theory, a theoretical framework of urban housing investment resilience is constructed. Spatiotemporal analysis and qualitative comparison methods are used to divide 35 large and medium-sized cities into two categories, first-tier and non-first-tier cities, and their spatiotemporal characteristics and differences in terms of formation mechanism differences then explored. The results show that (1) the overall housing investment resilience of the 35 investigated cities is low, with the characteristics of periodic evolution, and there are obvious differences between the first-tier and non-first-tier cities as well as unbalanced development problems. (2) The three internal investment decision factors of returns, costs, and expectations and the five external support factors of economic growth, infrastructure development, labor market, policy regulation, and monetary policy do not, by themselves, constitute the necessary conditions for high levels of urban housing investment resilience, and there are three paths for the development of high levels of housing investment resilience in both first-tier and non-first-tier cities. (3) The twos types of cities have the same conformational path of "return- and cost-driven" but different dedicated conformational paths, including "cost-driven", "expectation- driven", and "return- and expectation-driven", and the core conditions in their driving paths are different, with real estate policy being the core condition in the path of first-tier cities and infrastructure development and labor markets being the core conditions in the path of non-first-tier cities. (4) There is a potential substitution relationship between the three configuration paths of first-tier and non-first-tier cities, and the substitution relationship between the two types of cities is also different. The findings of this study reveal that the complex interaction between the urban resilience system, represented by infrastructure construction, and multiple factors, including the three elements of investment, can have an impact on the resilience of real estate investment.

『주택연구』의 30년간 연구경향: 텍스트 마이닝 접근법

We analyze the research trend of the Housing Research Review over the period of 1993 to 2021. In particular, adopting text-mining methodologies: word frequency analysis, wordcloud technique, N-gram, and Topic Modeling, we analyze titles and keywords of all the papers on the journal. Based on those analyses, we find that words that the papers on the journal most frequently used over the past 30 years are ‘housing’ and ‘research’, which are followed by ‘price’, ‘househhold’ and ‘income’. Our word proximity analsyis shows the most-related keywords; housing prices, housing markets, fluctuations, and trading volume in terms of market and price structure; housing occupancy and household changes in terms of housing demand.
 Our analysis of Latent Dirichlet Allocation reveals six categories; ‘the ownership and apartments’, ‘price and space’, ‘real estate policy and regulation’, ‘household’s decision in housing market’, ‘regional characteristics evaluation’, and ‘residency and rental housing’. The most interested topics are related to ‘price’, and we confirm that the topics of interest has been changed over the decades; from ‘development and management’ and ‘rental housing and real estate’ to ‘public housing’. In addition, our analysis confirms that housing research on Housing Studies Review is comprehensive and includes a variety of housing topics.

주택시장에서 공급과 생산의 의미에 관한 연구

(1) RESEARCH OBJECTIVES The purpose of this paper is to argue that supply in the housing market should be viewed as a concept that combines the production of new housing and the sale of existing housing (existing housing supply)and to expand the concept of supply in the housing market considering interest rates and financial markets in terms of housing investment goods. (2) RESEARCH METHOD This study adopted the existing theoretical literature review and I added my thoughts to it. (3) RESEARCH FINDINGS I argued that the logic that supply is used similarly to production in general economics should be used in a clear distinction between production and supply in the housing market because of the properties of housing, such as permanence, buoyancy, and immobility. 2. RESULTS Considering that housing has characteristics such as durability, permanence, buoyancy, and immobility, unlike general goods, it is hoped that this new discussion on supply will help to understand the housing market and establish correct real estate policies.

해외 부동산 직접투자의 인식에 관한 연구

The purpose of this study is to investigate and analyze the perception of direct investment in overseas real estate. The research method combines theoretical research and case study. In the case study, 100 real estate-related workers were surveyed on their perceptions of overseas real estate direct investment, and this was statistically analyzed. Considering the results of the analysis, As a result of the analysis, first, 94% were positive about the need for overseas real estate direct investment, and only 4% were negative. Second, as for preferred investment destination countries, “developed countries such as the United States, Canada, and Europe” were high at 41% and “developing countries such as Vietnam, the Philippines, and Malaysia” were high at 32%. Third, 91% were positive about Japanese real estate investment. Fourth, as for the motives for overseas real estate direct investment, “domestic strong regulation-oriented real estate policy” was found to be high at 56%, and “opaque future of Korean society” was found to be high at 25%. Fifth, in terms of investment intention, “stable rate of return” was 28%, “cheap ownership tax and capital gains tax” was 27%, and “certainty of ownership guarantee” was 23%. Sixth, as obstacles to direct investment in overseas real estate, “problem of overseas real estate management” was found to be high at 53%, and “ignorance of overseas real estate market” was found to be high at 29%. Considering the results of this analysis, the government suggests that a policy to support direct overseas real estate direct investment is needed to reduce investment risk and ensure stable investment for overseas real estate direct investors.

공시가격 조정을 통한 재산세 개편에 관한 소고

Among the policies of ‘Real estate normalization’ of the ‘Yoon Suk-Yeol Government’s 120 national tasks’ announced on July 7, 2022, the adjustment of the announced real estate price has an important meaning. This is because this is a common factor related to the reconsideration of the reconstruction surplus profit recovery system and the real estate tax system, and is related to the downward revision of the reconstruction surplus profit recovery system and the reduction of the comprehensive real estate tax. The current government's policy is contradictory to the 'Plan for Realization of Official Real Estate Price' and 'Property Tax Relief Plan' announced by the Moon Jae-In government on November 11, 2020. The real estate price realization plan of the Moon Jae-In government reflects the 50-70% level to 90% of the market price by raising the announced real estate prices such as houses, buildings, and land by 3-4%p a year over 10 to 15 years. it will become a reality. In order to alleviate the burden of property tax while realizing the published price, the property tax rate of houses with a published price of 600 million won or less owned by a single homeowner should be reduced by 0.05%p for each tax base section. The policy for this is complementary. The current government's 'normalization' of the real estate tax system means that the announced real estate price will be returned to the level of 2020, that is, before the realization of the announced real estate price plan. This means that the upper limit will be lowered from 300% to 200% for the 2nd and 3rd homeowners in the adjustment area. Since the real estate tax consists of taxation on capital gains at the stage of acquisition, holding, and transfer of real estate, the tax burden on the people increases as the published price becomes real. According to the published price reflected up to 90% of the real estate market price, the land, building, and housing prices are evaluated as higher when calculating the property tax or comprehensive real estate tax. However, before denigrating the realization of the official price as a policy intended only to increase revenue or criticizing the ripple effect and side effects, it is necessary to consider the essential meaning of the realization of the official price in the real estate policy of the government. The main purpose of realizing the announced price is to secure the equity of the realization rate by real estate type and to achieve balance by reducing the difference in the market price reflection rate of the announced price by real estate price range. The steep rise in real estate prices, which exceeds income growth, causes problems such as inducing the gap between the rich and the poor, lowering the possibility of homeownership for non-homeowners, focusing on real estate investment rather than labor income, and insufficient social investment due to a decrease in disposable income. The realization of the announced price is also being used as a method to solve the problem of housing supply shortage to some extent by increasing the number of listings in the housing market by increasing the burden on multi-homeowners through an increase in the real estate holding tax. In addition to the adjustment of the official land price, the increase in the fair market value ratio is an important factor in reforming the real estate tax system. This is because, unlike housing, in the case of land, speculative factors such as price spikes are unclear, so it is a fair method to focus on the official land price and fair market value ratio rather than the tax rate. The property tax has characteristics as a local tax, such as the immobility of real estate and the regional universality of taxation, and the relationship between the benefits of public services provided by local governments and the tax burden is relatively clear.

시공사 브랜드가 아파트 분양가와 투자수익에 미치는 영향

Depending on the economic situation, real estate policies have been announced with the goal of stabilizing real estate or revitalizing the economy. Before implementing real estate policies, effective operation and evaluation of government policies are essential. Real estate exhibits different regional variations due to its unique location fixity, and as a consumer and investment good, it is a commodity that is affected in a complex way, such as liquidity, supply, macroeconomics, and psychology. Real estate policies vary by tax policies such as holding tax and capital gains tax, and housing finance regulations (LTV, DTI). From this point of view, there is a need for a tool that can analyze the comprehensive policy effects including various types of real estate policies of the government. In this study, the available variables among the macroeconomic variables of the real estate market macroeconomic model designed on the premise of the ripple path following the implementation of real estate policies and the apartment sales transaction price index in the metropolitan area and nationwide, which is a proxy for apartment houses, were set as variables and analyzed through multivariate analysis. Analyze dynamic variations between variables. The purpose of this study is to examine the effect of housing finance regulation on housing mortgage loans in the national and metropolitan areas among real estate policies and the effect of tax policies and mortgage loans on apartment house prices (apartment sale price index) among real estate policies. We would like to focus on analyzing the effectiveness of the policy.

시공사 브랜드가 아파트 분양가와 투자수익에 미치는 영향

Depending on the economic situation, real estate policies have been announced with the goal of stabilizing real estate or revitalizing the economy. Before implementing real estate policies, effective operation and evaluation of government policies are essential. Real estate exhibits different regional variations due to its unique location fixity, and as a consumer and investment good, it is a commodity that is affected in a complex way, such as liquidity, supply, macroeconomics, and psychology. Real estate policies vary by tax policies such as holding tax and capital gains tax, and housing finance regulations (LTV, DTI). From this point of view, there is a need for a tool that can analyze the comprehensive policy effects including various types of real estate policies of the government. In this study, the available variables among the macroeconomic variables of the real estate market macroeconomic model designed on the premise of the ripple path following the implementation of real estate policies and the apartment sales transaction price index in the metropolitan area and nationwide, which is a proxy for apartment houses, were set as variables and analyzed through multivariate analysis. Analyze dynamic variations between variables. The purpose of this study is to examine the effect of housing finance regulation on housing mortgage loans in the national and metropolitan areas among real estate policies and the effect of tax policies and mortgage loans on apartment house prices (apartment sale price index) among real estate policies. We would like to focus on analyzing the effectiveness of the policy.

CMOs의 가치평가에 관한 연구

This study empirically analyzed whether real estate policies have a significant effect on housing sales, jeonse prices, and land sales prices using MBS is not active in Korea due to difficulties in evaluation. In the appraisal of MBS, the default risk is a cost that someone has to bear even if the government agency removes it through relevant guarantees. In this study, five CMOs data are evaluated and analyzed through the financial model developed based on traditional capital budgeting theory, option pricing theory, and Cho & Jun(2019)’s model recognizing that housing price fluctuation is an important factor affecting the default risk in MBS. Finally, the results drawn from this study help provide a theoretical framework for the valuation of MBS and show relevant implications for rational decision-making to MBS-related entities.

Evaluation Model of Tourism-Driven Real Estate Economic Growth Point Based on AHP

The economy of real estate is intimately connected to people’s quality of life, living standards, and living situations, all of which are extremely important in economic life. In order to better understand the impact of tourism-driven real estate on economic growth, based on AHP, this paper establishes an evaluation model of tourism-driven real estate economic growth. Based on the influencing factors of tourism real estate development, the evaluation model is built in this study of the tourism real estate development model by AHP and determines the optimal model according to the evaluation results. Through the application of the AHP model, the effect of real estate regulation policy is evaluated. The housing prices are affected not only by the influencing factors of housing prices in the pressure indicators, but also by real estate control policies. The model can effectively assess the economic growth point of real estate, propose real estate-specific guiding alternatives for economic growth and development, and ensure that the real estate market develops in a healthy and orderly manner.

A model for Real Estate Price Prediction using Multi-Level Stacking Ensemble Technique

Recent research and economic publications have shown the impact of real estate investment on the over economy of Nigeria. It is therefore crucial to employ machine learning technique to predict the price for real estate properties. Real estate price analysis and prediction will assist in establishment of real estate policies and can also be used to aid real estate property stakeholders to come up with informative decisions without bias or prejudice. Thus, it is imperative to develop a model to improve the accuracy of real estate price prediction. The goal of this research is to develop a model using a multi-level stacking ensemble technique to predict price of real estate property. The dataset utilized for the study was collected from transactions done by real estate firms in Port Harcourt and it consist of a total of 1053 rows with twelve features. The base model used includes Random Forest(RF), Extreme Gradient Boosting Algorithm(XGBoost), Light Gradient Boosting Machine(LightGBM), Decision Tree regression and ElasticNet Regression. Various combinations of the base models were stacked using StackingCVRegressor. The final model was developed by combining the best performing stacked models and evaluated using R-Square, Mean Absolute Error(MAE), Root Mean Square Error(RMSE), Mean Square Error(MSE) and Training time. The proposed model outperformed the various individual base model with R-square of 0.985203, MSE of 0.013438, RMSE of 0.115923, MAE of 0.063411 and training time of 0.599398. The result show that multi-level stacking significant improve the accuracy of a model. Again, it was observed stacking improve the performance accuracy of a model at the cost of computational time. Stacking by using blending function for the proposed model significantly reduced the computational time for training the model to 0.599398 second when compared to using StackingCVRegressor with training time of 107.054931 seconds. Therefore, multi-level stacking ensemble technique can be employed to improve the predictive accuracy of a prediction model. Future work can be done by increasing the dataset and also increasing the number of features.

South Korea in 2021

The two biggest stories of 2021 in South Korea were a cutthroat presidential nomination and the nationwide outcry over soaring housing prices. Both the ruling and opposition parties had gone through a noisy and murky nomination process. In 2021, housing prices continued to rise, causing citizens’ anxiety and anger to reach their peak, and the president apologized for the failure of the real estate policy. Despite the maladministration, president Moon Jae-in, unlike previous presidents, did not become a lame duck. Meanwhile, the popularity of Hallyu, or the Korean Wave, continued with the success of the movie Minari, K-pop band BTS, and the Netflix drama series Squid Game.

KEBIJAKAN PERUBAHAN LAHAN DALAM PEMBANGUNAN FOOD ESTATE DI KALIMANTAN TENGAH

Permen LHK No. 24/2020 is a land use policy for Indonesia’s food security through food estates program. The program aims to increase the added value of local agricultural production in order to increase employment, low food prices, and the development of large-scale agricultural businesses. According to regulation issud by LHK, food estate policy has the potential to change forest areas into agricultural land because it is legalized by the government through Undang-Undang Cipta Kerja. The consequences of implementing this policy are including environmental damage, increased conflict and poor land use management which is not in accordance with the Central Kalimantan spatial plan (RTRWP). The food estate program becomes government's priority in achieving food security through the State supports in a fast and less bureaucracy process on land licensing. This indicates that the process of land clearing for food estate violates forestry and land use regulations.

Measuring Real Estate Policy Uncertainty in China

Measuring Real Estate Policy Uncertainty in China

Research on the Impact of Market Concern for Real Estate Policy on Housing Prices: Evidence from Internet Search and Hedonic Price Theory

To avoid the effects of systemic financial risks caused by extreme fluctuations in housing price, the Chinese government has been exploring the most effective policies for regulating the housing market. Measuring the effect of real estate regulation policies has been a challenge for present studies. This study innovatively employs big data technology to obtain Internet search data (ISD) and construct market concern index (MCI) of policy, and hedonic price theory to construct hedonic price index (HPI) based on building area, age, ring number, and other hedonic variables. Then, the impact of market concerns for restrictive policy, monetary policy, fiscal policy, security policy, and administrative supervision policy on housing prices is evaluated. Moreover, compared with the common housing price index, the hedonic price index considers the heterogeneity of houses and could better reflect the changes in housing prices caused by market supply and demand. The results indicate that (1) a long-term interaction relationship exists between housing prices and market concerns for policy (MCP); (2) market concerns for restrictive policy and administrative supervision policy effectively restrain rising housing prices while those for monetary and fiscal policy have the opposite effect. The results could serve as a useful reference for governments aiming to stabilize their real estate markets.

A Study on the Prediction of House Price Index in First‐Tier Cities in China Based on Heterogeneous Integrated Learning Model

To address the difficulty of low prediction accuracy, insufficient model stability, and certain lag associated with a single machine learning model in the prediction of house price, this paper proposes a multimodel fusion house price prediction model based on stacking integrated learning. Firstly, web search data affecting house prices were collected by web crawler technology, and Spearman correlation analysis was performed on the attribute set to reduce its complexity and establish a prediction index system for four first‐tier cities in China. Secondly, with the goal of improving accuracy, diversity, and generalization ability, the types of base learners as well as metalearners are determined, and the parameters of the base learners are optimized using the grey wolf optimization algorithm to produce the GWO‐stacking model, and the experimental results from four datasets demonstrate that the model has high prediction accuracy. Finally, to solve the issue of unintelligible black boxes in machine learning models, we have used the state‐of‐the‐art interpretation method SHAP combined with the LightGBM algorithm to interpret the model, and the result can be used as a basis for real estate policy planning and adjustment and even guide the demand of home buyers, thus improving the efficiency and effectiveness of government policy making.

USING SVR AND MRA METHODS FOR REAL ESTATE VALUATION IN THE SMART CITIES

Abstract. Determination of real estate value plays a very critical role in economic development and basic needs of people. Increasing demand for real estate together with population growth is making it difficult to determine real estate value. In applications where real estate is the main subject, such as urban activities, smart cities and urbanization, urban information system and valuation systems, model-based value estimations are essential for effective land/real estate policy. The type of real estate and impact degree of features depending on the type should be known as well as value estimation. It will be beneficial to follow a method that both determines the real estate value and factor impact degree. With the studies to be carried out using such methods, both region-specific valuation models can be created and the model is established with the optimum variable. This paper aimed to determine real estate value by using Support Vector Regression (SVR) and Multi Regression Analysis (MRA) methods for effective real estate management. Besides, both methods were examined by revealing the impact degrees of features that affect the value. The methods were applied to 319 parcels in Konya. For each parcel, 31 land features and market values were collected. The parcel data collected since 2018 were included in the models. From the results, the RBF-SVR model reached the highest R2 value with 0.88, while the MRA model reached 0.86.

Machine-Learning-Based Prediction of Land Prices in Seoul, South Korea

The accurate estimation of real estate value helps the development of real estate policies that can respond to the complexities and instability of the real estate market. Previously, statistical methods were used to estimate real estate value, but machine learning methods have gained popularity because their predictions are more accurate. In contrast to existing studies that use various machine learning methods to estimate the transactions or list prices of real estate properties without separating the building and land prices, this study estimates land price using a large amount of land-use information obtained from various land- and building-related datasets. The random forest and XGBoost methods were used to estimate 52,900 land prices in Seoul, South Korea, from January 2017 to December 2020. The models were also separately trained for different land uses and different time periods. Overall, the results revealed that XGBoost yields a higher prediction accuracy. Whereas the XGBoost models were more accurate on the 2020 data than on the 2017–2020 data when analyzing residential areas, the random forest models were more accurate on the 2017–2020 data than on the 2020 data. Further analysis will extend the prediction model to consider submarkets determined by price volatility and locality.