scholarly journals A Pricing Model for Urban Rental Housing Based on Convolutional Neural Networks and Spatial Density: A Case Study of Wuhan, China

2022 ◽  
Vol 11 (1) ◽  
pp. 53
Author(s):  
Hang Shen ◽  
Lin Li ◽  
Haihong Zhu ◽  
Feng Li
Keyword(s):  
Neural Networks ◽  
Housing Prices ◽  
Rental Housing ◽  
Housing Price ◽  
Coefficient Of Determination ◽  
Spatial Density ◽  
Pricing Models ◽  
One Dimensional ◽  
Relevant Variables ◽  
The Government

With the development of urbanization and the expansion of floating populations, rental housing has become an increasingly common living choice for many people, and housing rental prices have attracted great attention from individuals, enterprises and the government. The housing rental prices are principally estimated based on structural, locational and neighborhood variables, among which the relationships are complicated and can hardly be captured entirely by simple one-dimensional models; in addition, the influence of the geographic objects on the price may vary with the increase in their quantities. However, existing pricing models usually take those structural, locational and neighborhood variables as one-dimensional inputs into neural networks, and often neglect the aggregated effects of geographical objects, which may lead to fluctuating rental price estimations. Therefore, this paper proposes a rental housing price model based on the convolutional neural network (CNN) and the synthetic spatial density of points of interest (POIs). The CNN can efficiently extract the complex characteristics among the relevant variables of housing, and the two-dimensional locational and neighborhood variables, based on the synthetic spatial density, effectively reflect the aggregated effects of the urban facilities on rental housing prices, thereby improving the accuracy of the model. Taking Wuhan, China, as the study area, the proposed method achieves satisfactory and accurate rental price estimations (coefficient of determination (R2) = 0.9097, root mean square error (RMSE) = 3.5126) in comparison with other commonly used pricing models.

Housing Inflation: Policy Guidelines from Shariah Perspective

2019 ◽  
Vol 16 (1) ◽  
pp. 70-81
Author(s):  
Azrul Azlan Iskandar Mirza ◽  
Asmaddy Haris ◽  
Ainulashikin Marzuki ◽  
Ummi Salwa Ahmad Bustamam ◽  
Hamdi Hakiem Mudasir ◽  
...  
Keyword(s):  
Content Analysis ◽  
Housing Prices ◽  
Construction Materials ◽  
Housing Price ◽  
Developed Countries ◽  
Control Element ◽  
Recent Issue ◽  
Analysis Methodology ◽  
The Government ◽  
Developing And Developed Countries

The soaring housing prices in Malaysia is not a recent issue. It is a global phenomenon especially in developing and developed countries, driven by factors including land price, location, construction materials cost, demand, and speculation. This issue demands immediate attention as it affects the younger generation, most of whom could not afford to buy their own house. The government has taken many initiatives and introduced regulations to ensure that housing prices are within the affordable range. This article aims to introduce a housing price control element from the Shariah perspective, as an alternative solution for all parties involved in this issue. It adopts content analysis methodology on policy from Shariah approved sources.

Application of ARMA Regression Model of Housing Prices Influence Factors of Xi'an

2013 ◽  
Vol 405-408 ◽  
pp. 3340-3342
Author(s):  
Hui Zhi ◽  
Yue Fan Wang
Keyword(s):  
Real Estate ◽  
Regression Model ◽  
Housing Prices ◽  
Influence Factors ◽  
Housing Price ◽  
The Real Estate ◽  
Housing Price Index ◽  
Long Run ◽  
The Government ◽  
The Impact

By selecting the relevant factors affect the real estate price, with the qualitative analysis method to analyze the housing prices changes of Xi'an, and then establish ARMA regression model of the housing price index, found that the factors exist long-run co-integration. In order to better reflect the actual, the government policy as a dummy variable is introduced into the model to make regression results more significantly, showing that government policies play an important role in the control of the impact on real estate prices.

scholarly journals Incomplete Financial Markets and the Booming Housing Sector in China

2020 ◽  
Vol 20 (265) ◽  
Author(s):  
Tamim Bayoumi ◽  
Yunhui Zhao
Keyword(s):  
Financial Markets ◽  
Housing Prices ◽  
Housing Price ◽  
Structural Vulnerability ◽  
Demand And Supply ◽  
Government Interventions ◽  
Balance Sheets ◽  
Market Incompleteness ◽  
The Government ◽  
The Impact

Housing is by far the most important asset in Chinese households’ balance sheets. However, despite forceful and frequent government interventions, the rise in Chinese housing prices has not been contained as much as intended, a trend that has not been reversed by the COVID-19 shock. In this paper, we first provide some stylized facts and then a DSGE model (encompassing both demand and supply channels) to highlight the impact of a “slow-moving” structural vulnerability—financial market incompleteness—on China’s housing prices. The model implies that to eradicate the root causes of the rising housing price, policymakers need to go beyond the housing market itself; instead, it would be desirable to deepen financial markets because these markets would help channel financial resources to productive sectors rather than to housing speculation. This is particularly important in the COVID era because without addressing this structural vulnerability, the higher household savings and the government stimulus may fuel the housing bubble and sow seeds for a future crisis. The paper can also shed light on the housing markets in other economies that face similar vulnerabilities.

scholarly journals The Commodity Housing Price Analysis Model--Take Haikou (China) as an Example

2018 ◽  
Vol 10 (8) ◽  
pp. 43 ◽  
Author(s):  
Bing-Qian Liu ◽  
Xiao-Yan Cao ◽  
Qi-Fan Yang ◽  
Yuan-Biao Zhang
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Housing Price ◽  
Multiple Linear Regression Model ◽  
Recent Decade ◽  
Analysis Model ◽  
Residential Housing ◽  
Restriction Policy ◽  
Commercial Housing ◽  
The Government

In recent decade years, the real-estate industry in China has achieved unprecedented development. Correspondingly, the rapid rise in house prices has led the government to introduce a series of macro-control policies. Based on the main regulatory mechanism of the purchase restriction policy, we take Haikou as an example to analysis the probable influence on housing price. We first select indicators from three aspects: supply, demand, and macroeconomic environment, and then establish a gray correlation model to extract the key factors of strong correlation, that is, real- estate investment, CPI, residential housing construction area, residential housing completion area. Moreover, we establish a multiple linear regression model based on GM (1, n) to obtain the multi-function relationship between commercial housing prices and these four key indicators. After that, we establish a population- purchases demand function model to predict the price of commercial housing in the coming year after introducing the purchase restriction policy. More significantly, we conclude that the purchase restriction policy can effectively regulate housing prices in the short term, but the long-term effect is limit.

scholarly journals One-Dimensional Convolutional Neural Networks for Hyperspectral Analysis of Nitrogen in Plant Leaves

2021 ◽  
Vol 11 (24) ◽  
pp. 11853
Author(s):  
Razieh Pourdarbani ◽  
Sajad Sabzi ◽  
Mohammad H. Rohban ◽  
José Luis Hernández-Hernández ◽  
Iván Gallardo-Bernal ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Hyperspectral Data ◽  
Nitrogen Fertilizers ◽  
Coefficient Of Determination ◽  
One Dimensional ◽  
Leaf Analysis ◽  
Kjeldahl Method

Accurately determining the nutritional status of plants can prevent many diseases caused by fertilizer disorders. Leaf analysis is one of the most used methods for this purpose. However, in order to get a more accurate result, disorders must be identified before symptoms appear. Therefore, this study aims to identify leaves with excessive nitrogen using one-dimensional convolutional neural networks (1D-CNN) on a dataset of spectral data using the Keras library. Seeds of cucumber were planted in several pots and, after growing the plants, they were divided into different classes of control (without excess nitrogen), N30% (excess application of nitrogen fertilizer by 30%), N60% (60% overdose), and N90% (90% overdose). Hyperspectral data of the samples in the 400–1100 nm range were captured using a hyperspectral camera. The actual amount of nitrogen for each leaf was measured using the Kjeldahl method. Since there were statistically significant differences between the classes, an individual prediction model was designed for each class based on the 1D-CNN algorithm. The main innovation of the present research resides in the application of separate prediction models for each class, and the design of the proposed 1D-CNN regression model. The results showed that the coefficient of determination and the mean squared error for the classes N30%, N60% and N90% were 0.962, 0.0005; 0.968, 0.0003; and 0.967, 0.0007, respectively. Therefore, the proposed method can be effectively used to detect over-application of nitrogen fertilizers in plants.

scholarly journals International Real Estate Review

2020 ◽  
Vol 23 (3) ◽  
pp. 337-365
Author(s):  
Chien-Wen Peng ◽  
◽  
Jerry T. Yang ◽  
Tyler Yang ◽  
◽  
...  
Keyword(s):  
Affordable Housing ◽  
Net Present Value ◽  
House Prices ◽  
Housing Prices ◽  
Rental Housing ◽  
Housing Tenure ◽  
Price Ratio ◽  
Model Framework ◽  
Housing Tenure Choice ◽  
The Government

This paper develops a theoretical model for equilibrium rent-to-price ratios from the competition between households and investors in the housing market. Households make their housing tenure choice in terms of rent vs. buy such as minimizing the cost of occupying a housing unit. On the other hand, investors choose between investing in rental housing vs. other investment opportunities in order to maximize their net present value. In the face of limited housing inventory, households and investors bid against one another which determines the allocation of the housing units among households (owner occupied properties) and investors (rental properties). We derive the sensitivity of the equilibrium rent-to-price ratio with respect to various market parameters, and subsequently analyze their potential impacts on the homeownership rate in the community. We show that some government mortgage programs subsidize homeownership to increase the affordability of owning a house, but may also provide even more incentive to the housing investors. Unless the government can effectively control the eligibility of borrowers, such affordable mortgage programs could work against their objectives and lead to higher housing prices and lower homeownership rates. Our model framework can be used to analyze the potential impacts of some of the new affordable housing policies on house prices or homeownership rates before adopting them.

The Effect of Investments and Risk Based Capital (RBC) of Tabarru Funds on Company (Case Study of Sharia Insurance in Indonesia for 2014-2019)

2020 ◽  
Vol 8 (1) ◽  
pp. 87-97
Author(s):  
Nana Diana ◽  
Tati Apriani
Keyword(s):  
Life Insurance ◽  
Statistical Tests ◽  
Linear Regression Analysis ◽  
Sampling Technique ◽  
Multiple Linear Regression Analysis ◽  
Insurance Companies ◽  
Coefficient Of Determination ◽  
Verification Method ◽  
Investment Returns ◽  
The Government

This study aims to examine the influence of investment returns and Risk Based Capital (RBC) Tabarru Funds to the profit of sharia life insurance in Indonesia from 2014-2019. This study The type of this research is quantitative research with descriptive verification as a method. This research method uses descriptive verification method with quantitative approach. The data used in this study were sourced from the financial statements of Islamic life insurance companies in Indonesia for the 2014-2019 period. Then the data obtained were analyzed using multiple linear regression analysis and hypothesis testing consisting of t test and f test with the help of SPSS 21 software. The sampling technique uses non probability sampling with purposive sampling technique. Based on the results of the study it can be seen that the development of investment returns on Sharia Life Insurance in Indonesia has fluctuated and even suffered losses. While the development of Risk Based Capital (RBC) has increased and decreased but overall above 120% as determined by the government. Likewise, the profits earned in each year fluctuate. The results of statistical tests show that investment results partially have a positive effect on profit and Risk Based Capital (RBC) of Tabarru funds partially has a negative effect on profit. Simultaneously investment return and Risk Based Capital (RBC) affect on profit. In addition, the results of the coefficient of determination (R2) were obtained which obtained a value of 81%. This shows that the variable investment returns and Risk Based Capital (RBC) can affect earnings by 81% and the remaining 19% is influenced by other variables not used in this study.

FRAUD CONFIRMATION, CLIENT SATISFACTION AND CLIENT LOYALTY

2019 ◽  
Vol 1 (1) ◽  
pp. 39
Author(s):  
Ngurah Pandji Mertha Agung Durya
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Client Satisfaction ◽  
Audit Quality ◽  
Hypothesis Test ◽  
Linear Regression Analysis ◽  
Quality Attributes ◽  
Path Model ◽  
Coefficient Of Determination ◽  
The Government

<p>This study aims to find evidence, the influence of Audit Quality Attributes, Client Satisfaction and Client Loyalty, which are moderated by Fraud Confirmation. The research was conducted at the BKM, a community-based organization, formed by the Government, through the <em>Kotaku</em> Program. The research used Regression statistical analysis and conducted a hypothesis test. Regression analysis used includes Simple Linear Regression Analysis, Multiple Regression Analysis, and MRA Regression Analysis, and Path Model Linear Regression Analysis. This study also pays attention to the calculation of the coefficient of determination to give an idea of the ability of the model in explaining the phenomenon of Client Satisfaction and Client Loyalty. The result that both partially and simultaneously, Audit Quality Attributes, Fraud Confirmation affected Client Satisfaction and Loyalty. The research also succeeded in proving that Client Satisfaction mediates the effect of Audit Quality Attributes on Client Loyalty, but failed to provide empirical evidence, that the Fraud Confirmation moderated the effect of Audit Quality Attributes on Client Satisfaction and Loyalty. Contribution to audit practices, where it is important to realize Client Satisfaction through Audit Quality Attributes and Fraud Confirmation, especially in situations where Fraud acts are suspected.</p>

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