Development Of A Forecasting Model To Predict The Downturn And Upturn Of A Real Estate Market In The Inland Empire

Amidst the dramatic real estate fluctuations in the first decade of the twenty-first century, this study recognized that there is a necessity to create a real estate prediction model for future real estate ventures and prevention of losses such as the mortgage meltdown and housing bust. This real estate prediction model study sought to reinstall the integrity into the American building and development industry, which was tarnished by the sudden emergence of various publications offering get-rich-quick schemes. In the fast-paced and competitive world of lending and real estate development, it is becoming more complex to combine current and evolving factors into a profitable business model. This prediction model correlated past real estate cycle pinpoints to economical driving forces in order to create an ongoing formula. The study used a descriptive, secondary interpretation of raw data already available. Quarterly data was taken from the study's seven independent variables over a 24-year span from 1985 to 2009 to examine the correlation over two real estate cycles. Public information from 97 quarters (1985-2009) was also gathered on seven topics: consumer confidence, loan origination volume, construction employment statistics, migration, GDP, inflation, and interest rates. The Null hypothesis underwent a test of variance at a .05 level of significance. Multiple regression analysis uncovered that four of seven variables have correlated and could predict movement in real estate cycle evidence from previous data, based in the Inland Empire. GDP, interest rates, loan origination volume, and inflation were the four economical driving variables that completed the Inland Empire's real estate prediction model and global test. Findings from this study certify that there is correlation between economical driving factors and the real estate cycle. These correlations illustrate patterns and trends, which can become a prediction model using statistics. By interpreting and examining the data, this study believes that the prediction model is best utilized through pinpointing an exact numerical location by running calculations through the established global equation, and recommends further research and regular update of quarterly trends and movements in the real estate cycle and specific variables in the formula.

This book provides a comprehensive analysis of housing affordability under the economic reforms and social transformations in urban China. It also offers an overall review of the current government measures on the housing market and affordable housing policies in China. By introducing a dynamic affordability approach and residual income approach, the book allows us to capture the size of the affordability gap more accurately, to better identify policy targets, and to assess the effectiveness of current public policy. The unique database on urban household surveys and regional information on affordable housing projects serve to strengthen the analysis. The book offers theoretical and empirical insights for in-depth affordability studies and helps readers to understand the social impacts of market reforms and the role of government on the Chinese housing market.

This research presents a time series estimation and prediction methods with the use of classic and advanced forecasting tools. Our discussion about di erent time series models is supported by giving the experimental forecast results, performed on several macroeconomic variables. Also, the main section deal with the experience of using such data in econometric analysis. Besides, the implementation of SAS and R software improve the parameter estimation and forecasting accuracy. The objective in providing crucial statistical techniques is to enable government and investors to make informed decisions regarding real estate. Most importantly, we obtain how to add value to business and apply skills set real estate in a real world environment. Eventually, the summary of various existing forecasting models can provide information to develop an appropriate forecasting model which describes the inherent feature of the series.

This paper employs classical bivariate, factor augmented (FA), slab-and-spike variable selection (SSVS)-based, and Bayesian semi-parametric shrinkage (BSS)-based predictive regression models to forecast US real private residential fixed investment over an out-of-sample period from 1983:Q1 to 2011:Q2, based on an in-sample estimates for 1963:Q1 to 1982:Q4. Both large-scale (188 macroeconomic series) and small-scale (20 macroeconomic series) FA, SSVS, and BSS predictive regressions, as well as 20 bivariate regression models, capture the influence of fundamentals in forecasting residential investment. We evaluate the ex-post out-of-sample forecast performance of the 26 models using the relative average Mean Square Error for one-, two-, four-, and eight-quarters-ahead forecasts and test their significance based on the McCracken (2004, 2007) MSE-F statistic. We find that, on average, the SSVS-Large model provides the best forecasts amongst all the models. We also find that one of the individual regression models, using house for sale (H4SALE) as a predictor, performs best at the four- and eight-quarters-ahead horizons. Finally, we use these two models to predict the relevant turning points of the residential investment, via an ex-ante forecast exercise from 2011:Q3 to 2012:Q4. The SSVS-Large model forecasts the turning points more accurately, although the H4SALE model does better toward the end of the sample. Our results suggest that economy-wide factors, in addition to specific housing market variables, prove important when forecasting in the real estate market.

This paper compares the performance of different forecasting models of California house prices. Multivariate, theory-driven models are able to outperform atheoretical time series models across a battery of forecast comparison measures. Error correction models were best able to predict the turning point in the housing market, whereas univariate models were not. Similarly, even after the turning point occurred, error correction models were still able to outperform univariate models based on MSFE, bias, and forecast encompassing statistics and tests. These results highlight the importance of incorporating theoretical economic relationships into empirical forecasting models.

Chapter 1 focuses on model selection and model averaging, both of which are approaches for handling modelling uncertainties. I aim to supplement the literature by studying the class of OLS post-selection estimators. Inspired by the shrinkage averaging estimator (SAE) and the Mallows model averaging (MMA) criterion, I further propose a shrinkage MMA (SMMA) estimator for averaging high-dimensional sparse models. The Monte Carlo design features an expanding sparse parameter space and further considers the effect of the effective sample size and the degree of model sparsity on estimators' finite sample performances. I find that the SMMA outperforms when averaging high-dimensional sparse models. In Chapter 2, the conventional perfect competition model is inadequate for the heterogeneous, illiquid, and decentralized housing market, which clears via multiple local time-varying equilibria. I first propose a spatial search model that caters to such market characteristics and provides theoretical micro-foundations to motivate the econometric model. Then, I introduce a nonlinear spatiotemporal autoregressive model with autoregressive disturbances (NLSTARAR) and augmented by local time-varying factors to unify the current hedonic pricing framework and uncover the real estate market structure by simultaneously identifying the spatiotemporal structure of the market's spatial dependence and its interaction with the housing market microstructure. To address model uncertainty, I propose both model selection and model averaging estimation strategies. Chapter 3 applies the methodologies developed in Chapter 2 to study the Greater Toronto Area (GTA) real estate market using a unique GTA dataset. The NLSTARAR model captures the effects of the local time-varying market microstructure besides the hedonic, demographic, and policy effects on the housing market. By model selection, I show that the real estate pricing is driven by a local time-varying market structure that effectively responds to the heterogeneity in assets consistent with existing theories. The local time-varying market microstructure dominates the spatial spillover effects with unexpected market shocks generating the market volatility. I further employ the rolling window approach to show that the uncovered real estate market structure captures the shifts in the market state, evolves as a market pricing mechanism, and better forecasts the real estate market out-of-sample.

This paper employs a probit model and a Markov switching model using information from the Conference Board Leading Indicator series to detect the turning points in four key US commercial rents series. We find that both the approaches based on the leading indicator have considerable power to predict changes in the direction of commercial rents up to two years ahead, exhibiting strong improvements over a naïve model, especially for the warehouse and apartment sectors. The empirical support for the adequacy of these prediction methodologies, from both in-sample and real time forecasting assessments, makes them a valuable tool to real estate professionals forecasting the US real estate markets. We find that while the Markov switching model nominally appears to be more successful in predicting periods of negative growth, it lags behind actual turnarounds in market outcomes whereas the probit is able to detect turning points several quarters ahead.

The need for projections in city planning and the social sciences were examined. The differences between forecasts and projections were discussed and various forecasting methods surveyed. The relationship between forecast types and planning applications was described. The Potential Housing Demand Model (PHD Model) of Canada Mortgage and Housing Corporation was examined in detail. An overview of the functioning of the model was presented along with an examination of the three main components of the model: the population projection model, the household projection model and the potential housing demand projection model. Methods of forecast appraisal were reviewed. The main criteria used by researchers were discussed. Accuracy and theoretical coherence were considered the primary criteria of appraisal by all major researchers in this field of inquiry. Correlates of accuracy were presented and discussed. Finally, a representative sample of past research was summarized and the main findings of these investigations presented. The rationale of the study was addressed and the following two objectives were stated: (1) To appraise the performance of the Potential Housing Demand Model in a manner consistent with past research. (2) To determine the performance of the PHD model with respect to the following two general findings of past research: (A) A forecast time horizon is the strongest and most consistent correlate of its accuracy. (B) The essential importance of the validity of core assumptions. The results of the case study analysis generally agreed with the two findings of past research except in the household demand component of the model which did not overestimate the overall level of households less accurately as the forecast horizon extended farther out. Also, in contrast to past research, the PHD model actually predicted a more accurate value for household growth for the entire 25 year period than it did for any 5 year census period.