Search results for: housing price prediction

Authors: Simin Eydivand, Ali Ghanadieslami, Reza Amiri

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Natural gas liquids (NGLs) are light hydrocarbons that are dissolved in associated or non‐associated natural gas in a hydrocarbon reservoir and are produced within a gas stream. There are different ways to calculate the price of NGL. In this study, a spreadsheet calculation method is used for calculation of NGL price with an attractive economy of IRR 25%. For a typical NGL Plant with 3,200,000 t/y capacity of investment and operation of 90% capacity to have IRR 25%, the price of NGL is calculated 277 $/t.

Keywords: natural gas liquid, NGL, LPG, price, NGL fractionation, NF, investment, IRR, NPV

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Authors: Min Chen, Rubi Gupta

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Understanding and predicting stock market movements is a challenging problem. It is believed stock markets are partially driven by public sentiments, which leads to numerous research efforts to predict stock market trend using public sentiments expressed on social media such as Twitter but with limited success. Recently a microblogging website StockTwits is becoming increasingly popular for users to share their discussions and sentiments about stocks and financial market. In this project, we analyze the text content of StockTwits tweets and extract financial sentiment using text featurization and machine learning algorithms. StockTwits tweets are first pre-processed using techniques including stopword removal, special character removal, and case normalization to remove noise. Features are extracted from these preprocessed tweets through text featurization process using bags of words, N-gram models, TF-IDF (term frequency-inverse document frequency), and latent semantic analysis. Machine learning models are then trained to classify the tweets' sentiment as positive (bullish) or negative (bearish). The correlation between the aggregated daily sentiment and daily stock price movement is then investigated using Pearson’s correlation coefficient. Finally, the sentiment information is applied together with time series stock data to predict stock price movement. The experiments on five companies (Apple, Amazon, General Electric, Microsoft, and Target) in a duration of nine months demonstrate the effectiveness of our study in improving the prediction accuracy.

Keywords: machine learning, sentiment analysis, stock price prediction, tweet processing

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Authors: Mengna Hu

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This paper examines the impact of an anti-speculation tax policy on the trading activities and home price movements in the housing market in Hong Kong. The study focuses on the secondary residential property market where transactions dominate. The policy intervention substantially raised the transaction cost to speculators as well as genuine homeowners who dispose their homes within a certain period. Through the demonstration of structural breaks, our empirical results show that the rise in transaction cost effectively reduced speculative trading activities. However, it accelerated price increase in the small-sized segment by vastly demotivating existing homeowners from trading up to better homes, causing congestion in the lower-end market where the demand from first-time buyers is still strong. Apart from that, by employing regime switching approach, we further show that the unintended consequences are likely to be persistent due to this policy together with other strengthened cooling measures.

Keywords: transaction costs, housing market, structural breaks, regime switching

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.

Keywords: apartment complex, big data, life-cycle building value analysis, machine learning

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Authors: Murat Yildiz, Burcu Yildiz

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Price Cap Regulation is a form of economic regulation designed in the 1980s in the United Kingdom. Price cap regulation sets a cap on the price that the utility provider can charge. The cap is set according to several economic factors, such as the price cap index, expected efficiency savings and inflation. It has been used by several countries as a regulatory regime in several sectors. Container port privatization is still in early stages in some countries. Lack of a general framework can be an impediment to privatization. This paper aims a general framework to comprising decisions to be made for variables which are able to accommodate the variety of container terminals. Several approaches that may be needed as well as a passage between approaches.

Keywords: Price Cap Regulation, ports privatization, container terminal price regime, earning sharing

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Authors: Antoni Wibowo, Harry Pujianto, Dewi Retno Sari Saputro

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The stock market can provide huge profits in a relatively short time in financial sector; however, it also has a high risk for investors and traders if they are not careful to look the factors that affect the stock market. Therefore, they should give attention to the dynamic fluctuations and movements of the stock market to optimize profits from their investment. In this paper, we present a nonlinear autoregressive exogenous model (NARX) to predict the movements of stock market; especially, the movements of the closing price index. As case study, we consider to predict the movement of the closing price in Indonesia composite index (IHSG) and choose the best structures of NARX for IHSG’s prediction.

Keywords: NARX (Nonlinear Autoregressive Exogenous Model), prediction, stock market, time series

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Authors: Josip Visković, Ana Rimac Smiljanić, Ines Ivić

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Housing loans play an important role in CEE countries’ economies. This fact is based on their share in total loans to households and their importance for economic activity and growth in CEE countries. Therefore, it is important to find out key determinants of housing loans demand in these countries. The aim of this study is to research and analyze the determinants of the demand for housing loans in Croatia. In this regard, the effect of economic activity, loan terms and real estate prices were analyzed. Also, the aim of this study is to find out what motivates people to take housing loans. Therefore, primarily empirical study was conducted among the Croatian residents. The results show that demand for housing loans is positively affected by economic growth, higher personal income and flexible loan terms, while it is negatively affected by interest rate rise.

Keywords: CEE countries, Croatia, demand determinants, housing loans

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Authors: Xuran Zhang, Huiru Chen

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In recent years, China has made great efforts to cultivate and develop the housing rental market, especially the rental-type public housing, which has been paid attention to by all sectors of the society. This paper takes Hangzhou rental-type public housing as the research object, and divides it into three development stages according to the different supply modes of rental-type public housing. Through data collection and field research, the paper summarizes the spatial characteristics of rental-type public housing from the five perspectives of spatial planning, spatial layout, spatial integration, spatial organization and spatial configuration. On this basis, the paper proposes the optimization of the spatial layout. The study concludes that the spatial layout of rental-type public housing should be coordinated with the development of urban planning. When planning and constructing, it is necessary to select more mixed construction modes, to be properly centralized, and to improve the surrounding transportation service facilities.  It is hoped that the recommendations in this paper will provide a reference for the further development of rental-type public housing in Hangzhou.

Keywords: Hangzhou, rental-type public housing, spatial distribution, spatial optimization

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Authors: Khalid Mujaljal, Hassan Alhajhoj

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This paper empirically investigates the influence of oil price fluctuations on the key macroeconomic variables of the Kingdom of Saudi Arabia using unrestricted VAR methodology. Two analytical tools- Granger-causality and variance decomposition are used. The Granger-causality test reveals that almost all specifications of oil price shocks significantly Granger-cause GDP and demonstrates evidence of causality between oil price changes and money supply (M3) and consumer price index percent (CPIPC) in the case of positive oil price shocks. Surprisingly, almost all specifications of oil price shocks do not Granger-cause government expenditure. The outcomes from variance decomposition analysis suggest that positive oil shocks contribute about 25 percent in causing inflation in the country. Also, contribution of symmetric linear oil price shocks and asymmetric positive oil price shocks is significant and persistent with 25 percent explaining variation in world consumer price index till end of the period.

Keywords: Granger causality, oil prices changes, Saudi Arabian economy, variance decomposition

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Authors: Mukul Anand

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Quality oriented affordable shelter for all has always been the key issue in the housing sector of our country. Homelessness is the acute form of housing need. It is a paradox that in spite of innumerable government initiated programmes for affordable housing, certain section of society is still devoid of shelter. About nineteen million (18.78 million) households grapple with housing shortage in Urban India in 2012. In Indian scenario there is major mismatch between the people for whom the houses are being built and those who need them. The prime force faced by public authorities in facilitation of quality housing for all is high cost of construction. The present paper will comprehend executable techniques for dilution of cost factor in housing the homeless. The key actors responsible for delivery of cheap housing stock such as capacity building, resource optimization, innovative low cost building material and indigenous skeleton housing system will also be incorporated in developing these techniques. Time performance, which is an important angle of above actors, will also be explored so as to increase the effectiveness of low cost housing. Along with this best practices will be taken up as case studies where both conventional techniques of housing and innovative low cost housing techniques would be cited. Transportation consists of approximately 30% of total construction budget. Thus use of alternative local solutions depending upon the region would be covered so as to highlight major components of low cost housing. Government is laid back regarding base line information on use of innovative low cost method and technique of resource optimization. Therefore, the paper would be an attempt to bring to light simpler solutions for achieving low cost housing.

Keywords: construction, cost, housing, optimization, shelter

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Authors: Cristian Pauna

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Trading the financial markets is a widespread activity today. A large number of investors, companies, public of private funds are buying and selling every day in order to make profit. Algorithmic trading is the prevalent method to make the trade decisions after the electronic trading release. The orders are sent almost instantly by computers using mathematical models. This paper will present a price prediction methodology based on a multi-dimensional neural network. Using the Fisher transform, the neural network will be instructed for a low-latency auto-adaptive process in order to predict the price evolution for the next period of time. The model is designed especially for algorithmic trading and uses the real-time price series. It was found that the characteristics of the Fisher function applied at the nodes scale level can generate reliable trading signals using the neural network methodology. After real time tests it was found that this method can be applied in any timeframe to trade the financial markets. The paper will also include the steps to implement the presented methodology into an automated trading system. Real trading results will be displayed and analyzed in order to qualify the model. As conclusion, the compared results will reveal that the neural network methodology applied together with the Fisher transform at the nodes level can generate a good price prediction and can build reliable trading signals for algorithmic trading.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, neural network

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Authors: John Abubakar

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Access to decent housing is a global challenge. An estimated one billion people currently live in slum settlements globally. About 80 percent of these slum dwellers are in Asia and Africa. Nigeria accounts for a significant percentage of African slum dwellers because of its size. Addressing the challenge of slum settlement in Nigeria can have far reaching positive implications in Africa. A major slum settlement in Nigeria is Mararaba slum in Karu local government of Nasarawa state. The importance of this slum settlement hinges on its proximity to Abuja, Nigeria’s capital city. This study is an attempt at identifying the impact of self-help housing and government mass housing scheme in addressing the problem of housing access in Mararaba area of Karu local government, Nasarawa state. The research method used is the content analysis of existing literature. After the review of existing literature, the paper argues that self-help house is more impactful in addressing housing access in Mararaba area of Karu local government. Therefore, self-help housing should be recognized and incorporated into the housing policy of Nasarawa state. Both self-help housing and government mass housing programs are reviewed comparatively, and their strengths and weaknesses analyses.

Keywords: slum settlement, informal settlement, progressive improvement, holistic planning

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Authors: Lufei Mao, Hongwei Chen, Zijiao Chai

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Informal housing is a widespread phenomenon in developing countries. In many newly-emerging cities in China, rapid urbanization leads to an influx of population as well as a shortage of housing. Under this background, container housing, a new form of informal housing, gradually appears on a small scale on urban temporary land in recent years. Container housing, just as its name implies, transforms containers into small houses that allow migrant workers group to live in it. Scholars in other countries have established sound theoretical frameworks for informal housing study, but the research fruits seem rather limited on this small scale housing form. Unlike the cases in developed countries, these houses, which are outside urban planning, bring about various environmental, economic, social and governance issues. Aiming to figure out this new-born housing form, a survey mainly on two container housing settlements in Hangzhou, China was carried out to gather the information of them. Based on this thorough survey, the paper concludes the features and problems of infrastructure, environment and social communication of container housing settlements. The result shows that these containers were lacking of basic facilities and were restricted in a small mess temporary land. Moreover, because of the deficiency in management, the rental rights of these containers might not be guaranteed. Then the paper analyzes the factors affecting the formation and evolution of container housing settlements. It turns out that institutional and policy factors, market factors and social factors were the main three factors that affect the formation. At last, the paper proposes some suggestions for the governance of container housing and the utility pattern of urban temporary land.

Keywords: container housing, informal housing, urban temporary land, urban governance

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Authors: Dai-Ling Li, Jung-Fa Cheng, Min-Lang Huang, Yun-Yao Chi

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The hedonic pricing model has been popularly applied to describe the economic value of environmental amenities in urban housing, but the results for cultural heritage variables remain relatively ambiguous. In this paper, integrated variables extending by GIS-based data and an existing typology of communities used to examine how cultural heritage and environmental amenities and disamenities affect housing prices across urban communities in Tainan, Taiwan. The developed models suggest that, although a sophisticated variable for central services is selected, the centrality of location is not fully controlled in the price models and thus picked up by correlated peripheral and central amenities such as cultural heritage, open space or parks. Analysis of these correlations permits us to qualify results and present a revised set of relatively reliable estimates. Positive effects on housing prices are identified for views, various types of recreational infrastructure and vicinity of nationally cultural sites and significant landscapes. Negative effects are found for several disamenities including wasteyards, refuse incinerators, petrol stations and industries. The results suggest that systematic hypothesis testing and reporting of correlations may contribute to consistent explanatory patterns in hedonic pricing estimates for cultural heritage and landscape amenities in urban.

Keywords: hedonic pricing model, cultural heritage, landscape amenities, housing

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Authors: Inês Ramalhete, Hugo Farias, Rui da Silva Pinto

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Adequate housing has been a widely discussed theme in academic circles related to low-cost housing, whereas its physical features are easy to deal with, overcrowding (related to social, cultural and economic aspects) is still ambiguous, particularly regarding the set of indicators that can accurately reflect and measure it. This paper develops research on low-cost housing models for developing countries and what is the best method to embed overcrowding as an important parameter for adaptability. A critical review of international overcrowding indicators and their application in two developing countries, Cape Verde and Angola, is presented. The several rationales and the constraints for an accurate assessment of overcrowding are considered, namely baseline data (statistics), which can induce misjudgments, as well as social and cultural factors (such as personal choices of residents). This paper proposes a way to tackle overcrowding through housing adaptability, considering factors such as physical flexibility, functional ambiguity, and incremental expansion schemes. Moreover, a case-study is presented to establish a framework for the theoretical application of the proposed approach.

Keywords: adaptive housing, low cost housing, overcrowding, housing model

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Authors: S. Avdasheva, D. Tsytsulina

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In many regulated industries over the world price cap as a method of price regulation replaces cost-plus pricing. It is a kind of incentive regulation introduced in order to enhance productive efficiency by strengthening sellers’ incentives for cost reduction as well as incentives for more efficient pricing. However pricing under cap is not neutral for competition in the market. We consider influence on competition on the markets where benchmark for cap is chosen from when sellers are multi-market. We argue that the impact of price cap regulation on market competition depends on the design of cap. More specifically if cap for one (regulated) market depends on the price of the supplier in other (non-regulated) market, there is sub-type of price cap regulation (known in Russian tariff regulation as ‘netback minus’) that enhance incentives to collude in non-regulated market.

Keywords: price regulation, competition, collusion

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Authors: Lin-Han Chiang Hsieh, Hsiao-Yi Lin

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This study aims to determine the impact of the disclosure of flood potential map on housing prices. The disclosure is supposed to mitigate the market failure by reducing information asymmetry. On the other hand, opponents argue that the official disclosure of simulated results will only create unnecessary disturbances on the housing market. This study identifies the impact of the disclosure of the flood potential map by comparing the hedonic price of flood potential before and after the disclosure. The flood potential map used in this study is published by Taipei municipal government in 2015, which is a result of a comprehensive simulation based on geographical, hydrological, and meteorological factors. The residential property sales data of 2013 to 2016 is used in this study, which is collected from the actual sales price registration system by the Department of Land Administration (DLA). The result shows that the impact of flood potential on residential real estate market is statistically significant both before and after the disclosure. But the trend is clearer after the disclosure, suggesting that the disclosure does have an impact on the market. Also, the result shows that the impact of flood potential differs by the severity and frequency of precipitation. The negative impact for a relatively mild, high frequency flood potential is stronger than that for a heavy, low possibility flood potential. The result indicates that home buyers are of more concern to the frequency, than the intensity of flood. Another contribution of this study is in the methodological perspective. The classic hedonic price analysis with OLS regression suffers from two spatial problems: the endogeneity problem caused by omitted spatial-related variables, and the heterogeneity concern to the presumption that regression coefficients are spatially constant. These two problems are seldom considered in a single model. This study tries to deal with the endogeneity and heterogeneity problem together by combining the spatial fixed-effect model and geographically weighted regression (GWR). A series of literature indicates that the hedonic price of certain environmental assets varies spatially by applying GWR. Since the endogeneity problem is usually not considered in typical GWR models, it is arguable that the omitted spatial-related variables might bias the result of GWR models. By combing the spatial fixed-effect model and GWR, this study concludes that the effect of flood potential map is highly sensitive by location, even after controlling for the spatial autocorrelation at the same time. The main policy application of this result is that it is improper to determine the potential benefit of flood prevention policy by simply multiplying the hedonic price of flood risk by the number of houses. The effect of flood prevention might vary dramatically by location.

Keywords: flood potential, hedonic price analysis, endogeneity, heterogeneity, geographically-weighted regression

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

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This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

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Authors: Asimiyu Mohammed Jinadu

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Housing is a basic necessity of life. Housing plays a central role in the life of living organisms as it provides the basic platform for the life support systems in human settlements. It is considered a social service and a basic right. Despite the importance of housing, Nigeria as a nation is faced with the problem of quantitative and qualitative shortfall in the number of housing units required to accommodate the citizens. This study examined the accessibility and affordability problems of low-income housing in Nigeria. It relied on secondary data obtained for the records of government ministries and agencies. Descriptive statistics were used in the analysis, and the information was presented in simple tables and charts. The findings show that over the years the government has provided serviced plots of land, owner occupier houses and mortgage loans for the people. As at 2016, the Federal Housing Authority (FHA) has completed a total of 23,038 housing units while another 14, 488 units were on-going under the Public Private Partnership scheme across the country. The study revealed that a total of 910, 671 housing units were proposed by the Government under the various low-income housing programmes between 1960 and 2017, but only 156, 336 units were delivered within the period, representing 17.17% success rate. Amongst others, the low-income group faced the problems of low access to and unaffordability of the few low-income housing delivered in Nigeria. The study recommended that all abandoned housing projects should be reviewed, rationalized, completed and made available to the targeted low-income people. Investment in micro housing finance, design and implementation of pro-poor housing programme and massive investment in innovative slum upgrading programmes by both the government and private sector are also recommended to ameliorate the housing problems of the low-income group in Nigeria.

Keywords: housing, low income group, problem, programme

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Authors: Bin Su

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The draft Auckland Unitary Plan outlines the future land used for new housing and businesses with Auckland population growth over the next thirty years. According to Auckland Unitary Plan, over the next 30 years, the population of Auckland is projected to increase by one million, and up to 70% of total new dwellings occur within the existing urban area. Intensification will not only increase the number of median or higher density houses such as terrace house, apartment building, etc. within the existing urban area but also change mean housing design data that can impact building thermal performance under the local climate. Based on mean energy consumption and building design data, and their relationships of a number of Auckland sample houses, this study is to estimate the future mean housing energy consumption associated with the change of mean housing design data and evaluate housing energy efficiency with the Auckland Unitary Plan.

Keywords: Auckland Unitary Plan, building thermal design, housing design, housing energy efficiency

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Authors: Rosdyana Mangir Irawan Kusuma, Wei-Chun Kao, Ho-Thi Trang, Yu-Yen Ou, Kai-Lung Hua

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Stock market prediction is still a challenging problem because there are many factors that affect the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment, and economic factors. This work explores the predictability in the stock market using deep convolutional network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a convolutional neural network model. This convolutional neural network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of the stock market. The effectiveness of our method is evaluated in stock market prediction with promising results; 92.2% and 92.1 % accuracy for Taiwan and Indonesian stock market dataset respectively.

Keywords: candlestick chart, deep learning, neural network, stock market prediction

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Authors: Timothy Akinwande, Eddie C. M. Hui

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Housing the urban poor has remained a challenge for many years across the world, especially in developing economies, despite the apparent research attention and policy interventions. It is apt to investigate the prevalent affordable housing (AH) provision challenges using unconventional approaches. It is pragmatic to thoroughly examine housing experts to provide supply-side solutions to AH challenges and investigate informal settlers to deduce solutions from AH demand viewpoints. This study being the supply-side investigation of an ongoing research, interrogated housing experts to determine significant expert solutions. Focus group discussions and in-depth interviews were conducted with housing experts in Nigeria. Through descriptive, content, and systematic thematic analyses of data, major findings are that deliberate finance models designed for the urban poor are the most significant housing finance solution in developing economies. Other findings are that adequately implemented rent control policies, deliberate PPP approaches like inclusionary housing and land-value capture, and urban renewal programmes to enlighten and tutor the urban poor on how to earn more, spend wisely, and invest in their own better housing will effectively solve AH finance challenges. Study findings are informative for the best approaches to achieve effective, affordable housing finance for the urban poor in Nigeria, which is indispensable for the achievement of sustainable development goals. This research’s originality lies in the exploration of experts’ opinions in relation to AH finance to produce an equation model of critical solutions to AH finance challenges. Study data are useful resources for future pro-poor housing studies. This study makes housing policy-oriented recommendations toward effective, affordable housing for the urban poor in developing countries.

Keywords: affordable housing, effective affordable housing, housing policy, housing research, sustainable development, urban poor

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Authors: Wangshi Yong, Wei Shi, Jing Zou, Qiang Li, Yilin Tian

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With the advancement of the reform of China’s housing security system, the impact is becoming increasingly profound. This paper explores the relationship between the housing security system and household entrepreneurship on the 2017 China Household Finance Survey (CHFS) and conducts a large number of robustness checks, including PSM and IV estimation. The results show that the assistance of the housing security system will significantly promote family entrepreneurship, increasing the probability of entrepreneurship by 2%. Its internal mechanism is mainly achieved by relaxing liquidity constraints and increasing household social capital. However, the risk preference effect has not existed. Heterogeneity analysis shows that the positive impact of the housing security system on family entrepreneurship is mainly reflected in areas with high housing prices and incomes, as well as households with long-term security and social or commercial insurance. Meanwhile, it also verifies that the positive externalities of the housing security system will also positively affect active entrepreneurial motivation, entrepreneurial intensity, and entrepreneurial innovation.

Keywords: the housing security system, household entrepreneurship, social capital, liquidity constraints, risk preference

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Authors: H. D. Ibrahim, H. C. Chinwenyi, T. Danjuma

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An option is defined as a financial contract that provides the holder the right but not the obligation to buy or sell a specified quantity of an underlying asset in the future at a fixed price (called a strike price) on or before the expiration date of the option. This paper examined two approaches for derivation of Partial Differential Equation (PDE) options price valuation formula for the Heston stochastic volatility model. We obtained various PDE option price valuation formulas using the riskless portfolio method and the application of Feynman-Kac theorem respectively. From the results obtained, we see that the two derived PDEs for Heston model are distinct and non-unique. This establishes the fact of incompleteness in the model for option price valuation.

Keywords: Black-Scholes partial differential equations, Ito process, option price valuation, partial differential equations

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Authors: Daijun Chen

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Affordable housing is subsidized by the government to meet the housing demand of low and middle-income urban residents in the process of urbanization and to alleviate the housing inequality caused by market-based housing reforms. It is a recognized fact that the living conditions of the insured have been improved while constructing the subsidized housing. However, the choice of affordable housing is mostly in the suburbs, where the surrounding urban functions and infrastructure are incomplete, resulting in the spatial mismatch of "jobs-housing" in affordable housing. The main reason for this problem is that the residents of affordable housing are more sensitive to the spatial location of their residence, but their selectivity and controllability to the housing location are relatively weak, which leads to higher commuting costs. Their real cost of living has not been effectively reduced. In this regard, 92 subsidized housing communities in Nanjing, China, are selected as the research sample in this paper. The residents of the affordable housing and their commuting Spatio-temporal behavior characteristics are identified based on the LBS (location-based service) data. Based on the spatial mismatch theory, spatial mismatch indicators such as commuting distance and commuting time are established to measure the spatial mismatch degree of subsidized housing in different districts of Nanjing. Furthermore, the geographically weighted regression model is used to analyze the influencing factors of the spatial mismatch of affordable housing in terms of the provision of employment opportunities, traffic accessibility and supporting service facilities by using spatial, functional and other multi-source Spatio-temporal big data. The results show that the spatial mismatch of affordable housing in Nanjing generally presents a "concentric circle" pattern of decreasing from the central urban area to the periphery. The factors affecting the spatial mismatch of affordable housing in different spatial zones are different. The main reasons are the number of enterprises within 1 km of the affordable housing district and the shortest distance to the subway station. And the low spatial mismatch is due to the diversity of services and facilities. Based on this, a spatial optimization strategy for different levels of spatial mismatch in subsidized housing is proposed. And feasible suggestions for the later site selection of subsidized housing are also provided. It hopes to avoid or mitigate the impact of "spatial mismatch," promote the "spatial adaptation" of "jobs-housing," and truly improve the overall welfare level of affordable housing residents.

Keywords: affordable housing, spatial mismatch, commuting characteristics, spatial adaptation, welfare benefits

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Authors: Mohammad Abdulaziz Algrnas, Emma Mulliner

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Housing projects have been established in Saudi Arabia, by both government and private construction companies, to meet the increasing demand from Saudi inhabitants across the country. However, the real estate market supply does not meet consumer preference requirements. Preferences normally differ depending on the consumer’s situation, such as the household’s sociological characteristics (age, household size and composition), resources (income, wealth, information and experience), tastes and priorities. Collecting information about consumer attitudes, preferences and perceptions is important for the real estate market in order to better understand housing demand and to ensure that this is met by appropriate supply. The aim of this paper is to identify consumer preferences for housing in Saudi Arabia. A quantitative closed-ended questionnaire was conducted with housing consumers in Saudi Arabia in order to gain insight into consumer needs, current household situation, preferences for a number of investigated housing attributes and consumers’ perceptions around the current housing problem. 752 survey responses were obtained and analysed in order to describe preferences for housing attributes and make comparisons between groups. Factor analysis was also conducted to identify and reduce the attributes. The results indicate a difference in preference according to the gender of the respondents and depending on their region of residence.

Keywords: housing attributes, Saudi Arabia, consumer preferences, housing preferences

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Authors: Guo Bingkun

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With the rapid development of China's social economy and the continuous improvement of urbanization level, people's living standards have undergone tremendous changes, and more and more people are gathering in cities. The demand for urban residents' housing has been greatly released in the past decade. The demand for housing and related construction land required for urban development has brought huge pressure to urban operations, and land prices have also risen rapidly in the short term. On the other hand, from the comparison of the eastern and western regions of China, there are also great differences in urban socioeconomics and land prices in the eastern, central and western regions. Although judging from the current overall market development, after more than ten years of housing market reform and development, the quality of housing and land use efficiency in Chinese cities have been greatly improved. However, the current contradiction between land demand for urban socio-economic development and land supply, especially the contradiction between land supply and demand for urban residential land, has not been effectively alleviated. Since land is closely linked to all aspects of society, changes in land prices will be affected by many complex factors. Therefore, this paper studies the factors that may affect urban residential land prices and compares them among eastern, central and western cities, and finds the main factors that determine the level of urban residential land prices. This paper provides guidance for urban managers in formulating land policies and alleviating land supply and demand. It provides distinct ideas for improving urban planning and improving urban planning and promotes the improvement of urban management level. The research in this paper focuses on residential land prices. Generally, the indicators for measuring land prices mainly include benchmark land prices, land price level values, parcel land prices, etc. However, considering the requirements of research data continuity and representativeness, this paper chooses to use residential land price level values. Reflects the status of urban residential land prices. First of all, based on the existing research at home and abroad, the paper considers the two aspects of land supply and demand and, based on basic theoretical analysis, determines some factors that may affect urban housing, such as urban expansion, taxation, land reserves, population, and land benefits. Factors of land price and correspondingly selected certain representative indicators. Secondly, using conventional econometric analysis methods, we established a model of factors affecting urban residential land prices, quantitatively analyzed the relationship and intensity of influencing factors and residential land prices, and compared the differences in the impact of urban residential land prices between the eastern, central and western regions. Compare similarities. Research results show that the main factors affecting China's urban residential land prices are urban expansion, land use efficiency, taxation, population size, and residents' consumption. Then, the main reason for the difference in residential land prices between the eastern, central and western regions is the differences in urban expansion patterns, industrial structures, urban carrying capacity and real estate development investment.

Keywords: urban housing, urban planning, housing prices, comparative study

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Authors: Deniz Peksen

Abstract:

This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

Procedia PDF Downloads 63

Authors: Jung-Hun Cho, Tae-Heon Moon, Jin-Hak Lee

Abstract:

Land price contains the comprehensive characteristics of urban space, representing the social and economic features of the city. Accordingly, land price can be utilized as an indicator, which can identify the changes of spatial structure and socioeconomic variations caused by urban development. This study attempted to explore the changes in land price by a new road construction. Methodologically, it adopted Space Syntax, which can interpret urban spatial structure comprehensively, to identify the relationship between the forms of road networks and land price. The result of the regression analysis showed the ‘integration index’ of Space Syntax is statistically significant and has a strong correlation with land price. If the integration value is high, land price increases proportionally. Subsequently, using regression equation, it tried to predict the land price changes of each of the lots surrounding the roads that are newly opened. The research methods or study results have the advantage of predicting the changes in land price in an easy way. In addition, it will contribute to planners and project managers to establish relevant polices and smoothing urban regeneration projects through enhancing residents’ understanding by providing possible results and advantages in their land price before the execution of urban regeneration and development projects.

Keywords: space syntax, urban regeneration, spatial structure, official land price

Procedia PDF Downloads 308

Authors: Sanchai Ratthanakwan

Abstract:

This research aims to study situations and characteristics of the problems facing the urban poor. The data and information are collected by focus group and in-depth interview leader and members of Four Regions Slum Network, community representatives and the social welfare officer. The research can be concluded that the problems of the urban poor faced with three major problems: Firstly, the shortage of housing and stability issues in housing; secondly, the problem of substandard quality of life; and thirdly, the debt problem. The study found that a solution will be found in two ways: First way is the creation of housing for the urban poor in slums or community intrusion by the state. Second way is the stability in the housing and subsistence provided by the community center called “housing stability”.

Keywords: urban poor, social welfare, Bangkok metropolis, housing stability

Procedia PDF Downloads 404