房产税制度下买卖行为影响的商品房价格波动系统动力学研究

在定性分析了居民人均可支配收入、实际房贷利率、信贷可获得性、住房空置面积及调控政策对房地产住宅价格影响的基础上, 提出了房地产住宅价格系统动力学模型, 重点研究了调控政策对住宅价格变动的影响。研究发现, 上调房产税税率对抑制房价攀升有较为明显的作用, 房产税税率不同, 房价的均衡价格也不同; 房价下降幅度与房产税税率间呈现一定的线性相关性。     

A hybrid fuzzy regression-fuzzy cognitive map algorithm for forecasting and optimization of housing market fluctuations

This paper presents a hybrid algorithm based on fuzzy linear regression (FLR) and fuzzy cognitive map (FCM) to deal with the problem of forecasting and optimization of housing market fluctuations. Due to the uncertainty and severe noise associated with the housing market, the application of crisp data for forecasting and optimization purposes is insufficient. Hence, in order to enable the decision-makers to make decisions with respect to imprecise/fuzzy data, FLR is used in the proposed hybrid algorithm. The best-fitted FLR model is then selected with respect to two indicators including Index of Confidence (IC) and Mean Absolute Percentage Error (MAPE). To achieve this objective, analysis of variance (ANOVA) for a randomized complete block design (RCBD) is employed. The primary objective of this study is to utilize imprecise/fuzzy data in order to improve the analysis of housing price fluctuations, in accordance with the factors obtained through the best-fitted FLR model. The secondary objective of this study is the exhibition of the resulted values in a schematic way via FCM. Hybridization of FLR and FCM provides a decision support system (DSS) for utilization of historical data to predict housing market fluctuation in the future and identify the influence of the other parameters. The proposed hybrid FLR-FCM algorithm enables the decision-makers to utilize imprecise and ambiguous data and represent the resulted values of the model more clearly. This is the first study that utilizes a hybrid intelligent approach for housing price and market forecasting and optimization.     

An economic agent-based model of coupled housing and land markets (CHALMS)

This paper describes a spatially disaggregated, economic agent-based model of urban land use, which is named for its innovative feature of coupled housing and land markets (CHALMS). The three types of agents—consumer, farmer and developer—all make decisions based on underlying economic principles, and heterogeneity of both individuals and the landscape is represented. CHALMS simulates the conversion of farmland to housing development over time, through the actions of the agents in the land and housing markets. Land and building structures in the housing bundle are treated explicitly, so the model can represent the effects of land and housing prices on housing density over time. We use CHALMS to simulate the dynamics of land-use changes as a representative suburban area grows. The presence of agent and landscape heterogeneity, stochastic processes, and path dependence require multiple model runs, and the expression of spatial dispersion of housing types, overall housing density, and land prices over time in terms of the most likely, or ‘average’, patterns. We find that CHALMS captures both the general tendency for diminishing population density at greater distances from the center city, and dispersed leapfrog patterns of development evident in most suburban areas of the US.     

A multi-agent model of urban processes: Modelling relocation processes and price setting in housing markets

This paper introduces an agent-based micro-simulation model of housing market processes. The model describes aggregate housing market developments, such as price and turnover, as the outcome of households’ decisions to search for a new dwelling, accept an offered dwelling or sell their dwelling. An important feature of the model is that households’ decisions are based on perceptions of housing market probabilities. Households update these perceptions based on observed bargaining outcomes in the market. The model was tested in a simulation experiment and appeared to respond plausibly to different market settings in terms of prices and households’ perception of the market.     

An estimation of housing vacancy rate using NPP-VIIRS night-time light data and OpenStreetMap data

ABSTRACT

As an informative proxy measure for a range of socio-economic variables, satellite-derived night-time light (NTL) data have been widely used to investigate the diverse anthropogenic activities and reveal urbanization development. Due to the rapid increase of Chinese urbanization rate, from 25.3% in 1987 to 58.5% in 2017, and ‘crazy expansion’ of city space, the sick phenomenon – ‘Ghost Town’ – has been brought out, generally defined as places with high housing vacancy rate (HVR), which will cause the huge waste of the limited land source in China. To investigate the HVR of urban areas in China, this study attempts to establish a hybrid model combining data derived from National Polar-Orbiting Partnership–Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL sensors with OpenStreetMap (OSM) data. By distinguishing non-residential areas and introducing detailed residential building information, we proposed a novel HVR estimation model, thus realizing the estimation of HVR in 31 Chinese provincial cities with different development levels (Tier 1–Tier 3). The results showed the average HVR of Tier 2 cities (0.204) was higher than that of Tier 1 cities (0.189) and Tier 3 cities (0.233). The model was proven more accurate (root mean square error of approximation (RMSE) = 0.022) when compared with previous models. To explore the reasons causing different HVRs in these provincial cities, the relationship between HVR and typical socio-economic factors – gross domestic product (GDP), population, and housing price – was also revealed. Through correlation verification and built of a regression model, HVR was found positively correlated with housing price (0.409), however, negatively correlated with population (?0.829) and GDP (?0.356). The research is an indication of the applicability of using data derived from NPP-VIIRS NTL sensors in reflecting HVR and an exploration to distinguish socio-economic factors influencing HVR in different cities. The model we proposed can potentially provide guidance for urban planners to formulate better land-use plan and rental measures.     

Gas flow meter for application in medical equipment for respiratory control: study of the housing

The study of the housing of a gas flow meter for use in medical equipment for respiratory control will be presented in this paper. The sensor is of the thermal type with dimensions 1.4 mm×0.9 mm. The housing is developed so as to assure conditions of laminar flow in a flow range from −200 to +200 standard liters per minute (SLPM). An analytical approach is first used to determine the main housing configuration. The type and the position of a bypass tube, which is fixed to the main flow tube, are studied in detail. Extensive simulations of the flow in different housing configurations and different conditions at the inlet and outlet of the housing were performed, using the finite element analysis (FEA) software package ANSYS. The optimum housing was finally fabricated and evaluated in oxygen flow. Excellent agreement of experimental results with simulation was obtained.     

An optimization model for location of subsidized housing in metropolitan areas

This paper presents an optimization model for evaluation of alternative spatial configurations of rent-subsidized housing in a large metropolitan area as well as associated monetary and nonmonetary impacts. Groups affected by these configurations include residents of subsidized housing, owners of nearby single-family housing, employers and society at large. Since impacts of subsidized housing are very localized, the first stage of the model creates potential location patterns for many small geographic areas. The second stage of the model uses local benefits to derive a location scheme for the metropolitan area which balances net social benefit with equity considerations of the geographical impact of subsidized housing. We apply this methodology to a small region in metropolitan Chicago and demonstrate alternative location schemes.     

Housing price forecasting based on genetic algorithm and support vector machine

Accurate forecasting for future housing price is very significant for socioeconomic development and national lives. In this study, a hybrid of genetic algorithm and support vector machines (G-SVM) approach is presented in housing price forecasting. Support vector machine (SVM) has been proven to be a robust and competent algorithm for both classification and regression in many applications. However, how to select the most appropriate the training parameter value is the important problem in the using of SVM. Compared to Grid algorithm, genetic algorithm (GA) method consumes less time and performs well. Thus, GA is applied to optimize the parameters of SVM simultaneously. The cases in China are applied to testify the housing price forecasting ability of G-SVM method. The experimental results indicate that forecasting accuracy of this G-SVM approach is more superior than GM.     

Computer-aided evaluation of the housing environments

This paper deals with the development and environmental variables involved in designing the housing layout form. It is considered that the factors taken into consideration and the crileria used in the course of decision-making in creating and reshaping the residential environments should be systematically tested in the light of the needs and social goals of the community. As the main potential of the computer lies in the process of analysis and appraisal the main objective of this study is to evaluate the quality and performance of the physical environment by means of utilizing computer techniques.     

基于ARIMA模型的二手房价格预测

 摘要： 近年来，我国一二线城市房价持续上涨，房屋成了人们日常生活讨论的热门话题，大家纷纷对未来的房价走势做出猜测。本文爬取国内某知名大型房产网站自2013年以来广州和深圳的二手房均价数据，采用ARIMA模型对未来的房价进行滚动预测，并使用RMSE对预测精度进行判断。结果表明，该模型可以对二手房均价进行持续预测，且预测精度较高，可为房屋买卖者提供参考。     

Decentralized markets and the emergence of housing wealth inequality

Recent studies suggest that the traditional determinants of housing wealth are insufficient to explain its current inequality levels. Thus, they argue that efforts should focus on understanding institutional factors. From the perspective of complex adaptive systems, institutions are more than the ‘the rules of the game’, they also consider the interaction protocols or the ‘algorithm’ through which agents engage in socioeconomic activities. By viewing markets as complex adaptive systems, I develop a model that allows estimating how much housing wealth inequality is attributable to the market institution. It combines virtues from two different modeling traditions: (1) the microeconomic foundations from overlapping-generation models and (2) the explicit interaction protocols of agent-based models. Overall, the model generates prices and housing inequality endogenously and from bottom-up; without needing to impose assumptions about the aggregate behavior of the market (such as market equilibrium). It accounts for economic and institutional factors that are important to housing consumption decisions (e.g., wages, consumption of goods, non-labor income, government transfers, taxes, etc.). I calibrate the model with the British Wealth and Assets Survey at the level of each individual household (i.e., ~25 million agents). By performing counter-factual simulations that control for data heterogeneity, I estimate that, in the United Kingdom, the decentralized protocol interaction of the housing market contributes with one to two thirds of the Gini coefficient. I perform policy experiments and compare the outcomes between an expansion in the housing stock, a sales tax, and an inheritance tax. The results raise concerns about the limitations of traditional policies and call for a careful re-examination of housing wealth inequality.     

Urban structures for lower-cost housing projects

The use of computers in the analysis and design of individual structures is known and widely used in practice. There are many operational computer programs such as ICES-STRUDL, ELAS, NASTRAN, WANG, and others which are readily available at the computation centers around the world. These programs are able to perform the analysis, either by direct stiffness method or by finite element technique of various structures and some of these programs will carry the study into complete design. The profession of structural engineering is fortunate to have an access to such a wealthy software library.The theme of this paper is not to deal with any individual computer program applicable to a particular structural unit, but to integrate the analysis and design computer programs with the total analysis and design of a housing project in an urban area. This concept defines an INTEGRATED SYSTEMS APPROACH to total analysis and design. The value of this approach stems from the fact that a structural engineer can not develop an efficient design for a structure in an urban project unless he is fully aware of all pertinent factors which are related to his structural unit. The Integrated Systems Approach will secure an optimum design for the project.Since one of the major concerns of our civilization is the provision of decent lower-cost housing for every citizen, structural engineers need to participate in this great endeavor by extending the application of their technical knowledge to its solution. Due to the size and complexity of the problem, the use of the computers is imperative. The Integrated Systems Approach is defined by a set of individual computer programs linked together. The structural analysis and design programs incorporated in this system use the data generated by the other general project study programs. It is obvious that the structural programs form the major part of the programming system. The Integrated Systems Approach to analysis and design stresses the point that the structural engineer should not consider only the factors for an optimum solution from the points of view of strength, durability and materials used. He has to be aware of other very pertinent socio-economic factors to develop a design to be happily accepted by the public.     

HouSI: Heuristic for delimitation of housing submarkets and price homogeneous areas

This paper seeks to address the problem of the empirical identification of housing market segmentation, once we assume that submarkets exist. The typical difficulty in identifying housing submarkets when dealing with many locations is the vast number of potential solutions and, in such cases, the use of the Chow test for hedonic functions is not a practical solution. Here, we solve this problem by undertaking an identification process with a heuristic for spatially constrained clustering, the “Housing Submarket Identifier” (HouSI). The solution is applied to the housing market in the city of Barcelona (Spain), where we estimate a hedonic model for fifty thousand dwellings aggregated into ten groups. In order to determine the utility of the procedure we seek to verify whether the final solution provided by the heuristic is comparable with the division of the city into ten administrative districts.     

Strategies for implementing decision technology systems successfully: a field study in army housing management

A decision technology system (DTS) has been proposed to provide direct support for the entire decision‐making process. In the 1990s, the concept was implemented in stages to facilitate US Army housing management. This implementation has been the first successful field application of the DTS theory. Recent enhancements confirmed earlier insights and offered important new lessons in developing, implementing, and benefiting from DTSs. This paper discusses the original insights and fresh lessons. To provide a context, the article first briefly describes the enhancements and presents a typical, but streamlined, system session. The paper then summarizes the documented gains from implementing the DTS theory, reports the utilized strategies, and infers the implications for the systems analysis and design process.     

Large scale spatial information systems in the built environment: An example applied to housing in the Third World

The efficient handling of land information within the urban environment, a requirement for sustainable city life, can be achieved with the application of spatial information systems (SIS). The particular application discussed in this paper is directed towards the recording and analysis of tenant-created extensions to government built housing, activity which has, in effect, turned housing consumers into housing producers. The customising of a commercial GIS is described, as is its potential use in case studies from Egypt, Ghana and Zimbabwe. Both neighbourhood structures and the layout of individual houses are capable of analysis. The integration of diverse data sets to create the SIS is outlined, as is the consequent need for comprehensive metadata. Implementation issues of SIS in the Third World are considered, along with further possible research themes such as the role of time, the topology of buildings, the use of SIS in demographics and the study of interior building circulation patterns.     

Minimizing army housing deficits with a DSS-delivered heuristic programming model

The ineffcient use of existing onpost, or available offpost, assets can result in insuffcient or unnecessary Army housing construction requests. Insuffcient requests can leave Army personnel inadequately housed, compromise morale and jeopardize military preparedness, while unnecessary requests can waste scarce natural resources and, in the process, alienate local residents, environmentalists and other interest groups. This paper: (a) presents a decision-support-system-delivered heuristic programming model that effciently utilizes scarce housing resources; and (b) demonstrates the significant economic and management benefits that can be achieved through the application of the model. The same concepts can be applied to the housing allocation problem faced by the other armed services, federal, state, local housing authorities and to related resource allocation problems in public and private enterprises.     

Determining the financial feasibility of privatized military housing initiatives with DSS-delivered quantitative models

The armed services must provide their personnel with acceptable housing at minimum cost within the vicinity of military installations. To help achieve these housing objectives, the Department of Defense (DoD) has entered into experimental joint ventures with private developers to construct attractive housing on military installation property. To support the analysis of the joint ventures, the DoD needed quantitative models that would help officials evaluate the feasibility of the housing projects. A decision support system, called the Housing Revitalization Support Office System (HRSOS), has been developed to deliver the necessary models. A key component of HRSOS is a collection of econometric, project occupancy, and simulation models that determine a project's financial outcomes. This paper describes the models, their role in the joint venture evaluation process, and the decision support system used to deliver the analyses and reports to DoD officials. The paper also presents the benefits from using the model and discusses the implications for public finance and military housing management.Scope and purposeHistorically, military housing has been acquired, maintained, and managed through the Department of Defense with public funds. The process has become quite expensive, and military personnel and officials have not always been satisfied with the resulting accommodations. To rectify the situation, the Department of Defense (DoD) established an agency, called the Housing Revitalization Support Office (HRSO), to develop innovative privatization initiatives that would improve the quality of military housing and reduce government expenditures for the accommodations. A decision support system, called the Housing Revitalization Support Office System (HRSO), has been developed to help government officials evaluate, among other things, the financial feasibility of the privatization initiatives. This paper presents the system and its underlying econometric and simulation models, examines the economic and management benefits from using the system, and discusses the implications for public finance and military housing management. The HRSOS situation represents a new application for operations research and other quantitative methodologies. In addition, it further illustrates how such methodologies can be delivered effectively to non-technical managers through decision support systems.     

Modelling housing typologies for urban redevelopment scenario planning

Increasing levels of urbanization, combined with growing populations and a need to manage urban redevelopment more sustainably has prompted the need for new tools for urban regeneration in established urban areas. While significant activity is occurring in the areas of volumetric analysis and 3D visualization, utilising these technologies in the development of urban planning tools requires a data schema for defining precinct objects for performance assessment while simultaneously addressing the complexity and interconnected nature of issues relevant to the urban built environment. This paper presents the outcomes of the research and development of a web-based 3D precinct visualization and assessment system, Envision Scenario Planner (ESP), which uses a library of housing typologies to generate easy-to-use, bottom-up, precinct-scale reports on residential infill. The paper illustrates how, through the specification of a residential precinct object data schema and the provision of a set of housing typologies, end users can quickly, and without domain knowledge, generate visualizations and assessments for a variety of housing scenarios, which allows them to determine fit-for-purpose solutions that address a range of issues relevant to contemporary planners and policy makers.     

House Price Appreciation in Old Age: Analysis and Issues for the Use of Reverse Mortgages as a Retirement Funding Strategy in Australia

This paper investigates whether the houses of Australian elderly home owners appreciate at below the market rate and examines the issues this may raise for the use of reverse mortgages as a retirement funding strategy in Australia. The viability of reverse mortgages where elderly home owners effectively borrow against their housing equity depends strongly on house prices appreciating enough to offset the outstanding loan balance at the end of the loan tenure. This paper’s findings indicate that after controlling for other influences, being aged 75 years or over lowers annual house price appreciation rate by almost 1.4 percentage points. Being aged 75 years or over also lowers home improvement expenditure by over AUD3,000 per year and this is found to be attributable to a decline in income during old age. The majority of elderly home owners want to protect at least half of their housing equity when considering participating in reverse mortgage programs, but given below-average house price appreciation rates during old age, the propensity of a 50% equity protection declines sharply with age. In particular, single females aged 75 years or over are least able to protect at least half of their housing equity, with only around 15% able to do so by the end of a reverse mortgage loan tenure. The paper also finds that, worryingly, elderly home owners with characteristics associated with slower house price appreciation rates are over-represented among reverse mortgage borrowers in Australia, namely, those aged 75 years or over, single, living in apartments or residing in states with relatively slow house price growth.     

五连杆整体桥的轴转向特性动力学分析

系统分析五连杆整体桥轴转向的原理及其特点.介绍五连杆整体桥的基本结构,将轴转向分为单纯的因受力产生的弹性变形轴转向和受力后由杆件运动产生的几何轨迹轴转向分别进行详细阐述.