Real Estate Modelling and Forecasting Hardcover_2

Introduction 11 involved with acquisitions, will rely more on their knowledge of the local-ity and building to make a buy or sell decision. This has also given rise to so-called ‘judgemental’ forecasts. Real estate markets have gone through severe cycles not predicted by bottom-up analysis, however, and thus this approach to forecasting has been questioned. For many, the winning for-mula is now not just having good judgement about the future direction of the market, but also making a careful quantitative analysis explaining cyclical movements and the impact of broader trends. Therefore, consistent withevidencefromotherfields,aviewthathasincreasinglygainedpopular-ity is that the optimal approach arises from a combination of judgemental andquantitativeforecasting.Moreover,thereisamoregenericeconometric and forecasting interest. Do quantitative techniques underperform judge-mental approaches or is the combination of quantitative and judgemental forecasts the most successful formula in the real estate market? The book addresses this issue directly, and the tools presented will give the reader a framework to assess such quandaries. Real estate forecasting can also be used for model selection. There are often competing theories available and it may be the case that there is more than one theory-consistent model that passes all the diagnostics tests set by the researcher. The past relative forecasting success of these models will guide model selection for future forecast production and other uses. Finally, forecasting is the natural progression in real estate as more data become available for a larger number of markets. In scholarly activity, the issue of data availability is highlighted constantly. One would expect that, withmoredataandmarkets,interestinrealestateforecastingwillcontinue to grow. The key objectives of forecasting in real estate are presented in box 1.1. Box 1.1 Objectives of forecasting work (1) Point forecasts. The forecaster is seeking the actual forecast value for rent growth or capital growth in one, two, three quarters or years, etc. (2) Direction forecasts. The forecaster is interested in the direction of the forecast and whether the trend is upward or downward (and perhaps an assessment can be made as to how steep this trend will be). (3) Turning point forecasts. The aim in this kind of forecast is to identify turning points or the possibility of a turning point. (4) Confidence. The modelling and forecasting process is used to attach a confidence interval to the forecast, how it can vary and with what probability. (5) Scenario analysis. This is the sensitivity of the forecast to the drivers of the model. The content of this book is more geared to help the reader to perform tasks one, two and five. 12 Real Estate Modelling and Forecasting 1.8 Econometrics in real estate, finance and economics: similarities and differences The tools that we use when econometrics is applied to real estate are funda-mentally the same as those in economic and financial applications. The sets of issues and problems that are likely to be encountered when analysing data are different, however. To an extent, real estate data are similar to economic data (e.g. gross domestic product [GDP], employment) in terms of their frequency, accuracy, seasonality and other properties. On the other hand, there are some important differences in how the data are generated. Realestatedatacan be generatedthroughthevaluationprocess ratherthan through surveys or government accounts, as is the case for economic data. There are some apparent differences with financial data, given their high frequency. A commonality with financial data, however, is that most real estate data are not subject to subsequent revisions, or, at least, not to the extent of economic data. In economics, a serious problem is often a lack of data to hand for testing the theory or hypothesis of interest; this is often called a small samples prob-lem. Such data may be annual and their method of estimation may have changed at some point in the past. For example, if the methods used to measure economic quantities changed twenty years ago then only twenty annual observations at most are usefully available. There is a similar prob-lem in real estate markets. Here, though, the problem concerns not only changing methods of calculation but also the point at which the data were first collected. In the United Kingdom, data can be found back to 1966 or earlier, but only at the national level. Databases such as the United King-dom’s Investment Property Databank (IPD) and that of the United States’ National Council of Real Estate Investment Fiduciaries (NCREIF) go back to the 1970s. In other regions, such as the Asia-Pacific retail markets, however, data are available only for about ten years. In general, the frequency dif-fers by country, with monthly data very limited and available only in some locations. As in finance, real estate data can come in many shapes and forms. Rents and prices that are recorded are usually the product of valuations that have been criticised as being excessively smooth and slow to adjust to changing market conditions. The problem arises from infrequent trading and trying to establish values where the size of the market is small. The industry has recognised this issue, and we see an increasing compilation of transactions data. We outlined in section 1.5 above that other real estate market data, such as absorption (a measure of demand), are constructed based on other market information. These data are subject to measurement error and revi-sions (e.g. absorption data are subject to stock and vacancy rate revisions Introduction 13 unless they are observed). In general, measurement error affects most real estate series; data revisions can be less serious in the real estate context compared with economics, however. Financial data are often considered ‘noisy’, which means that it is diffi-cult to separate underlying trends or patterns from random and uninteresting features. Noise exists in real estate data as well, despite their smoothness, and sometimes it is transmitted from the financial markets. We would con-sider real estate data noisier than economic data. In addition, financial data are almost always not normally distributed in spite of the fact that most techniques in econometrics assume that they are. In real estate, normality is not always established and does differ by the frequency of the data. The above features need to be considered in the model-building process, even if they are not directly of interest to the researcher. What should also be noted is that these issues are acknowledged by real estate researchers, valuers and investment analysts, so the model-building process is not hap-pening in a vacuum or with ignorance of these data problems. 1.9 Econometric packages for modelling real estate data As the title suggests, this section contains descriptions of various computer packages that may be employed to estimate econometric models. The num-berofavailablepackagesislarge,and,overtime,allpackageshaveimproved in the breadth of the techniques they offer, and they have also converged in terms of what is available in each package. Some readers may already be familiar with the use of one or more packages, and, if this is the case, this section may be skipped. For those who do not know how to use any econometrics software, or have not yet found a package that suits their requirements – read on. 1.9.1 What packages are available? Althoughthislistisbynomeansexhaustive,asetofwidelyusedpackagesis given in table 1.1. The programmes can usefully be categorised according to whether they are fully interactive (menu-driven), command-driven (so that the user has to write mini-programmes) or somewhere in between. Menu-driven packages, which are usually based on a standard Microsoft Windows graphical user interface, are almost certainly the easiest for novices to get started with, for they require little knowledge of the structure of the pack-age, and the menus can usually be negotiated simply. EViews is a package that falls into this category. On the other hand, some such packages are often the least flexible, since themenusofavailableoptionsare fixedbythedevelopers,andhence,ifone 14 Real Estate Modelling and Forecasting Table 1.1 Econometric software packages for modelling financial data Package software supplier EViews QMS Software Gauss Aptech Systems LIMDEP Econometric Software Matlab The MathWorks RATS Estima SAS SAS Institute Shazam Northwest Econometrics Splus Insightful Corporation SPSS SPSS Stata StataCorp TSP TSP International Note: Full contact details for all software suppliers can be found in the appendix at the end of this chapter. wishes to build something slightly more complex or just different, one is forced to consider alternatives. EViews has a command-based programming languageaswellasaclick-and-pointinterface,however,soitoffersflexibility as well as user-friendliness. 1.9.2 Choosing a package Choosing an econometric software package is an increasingly difficult task as the packages become more powerful but at the same time more homoge-neous.Forexample,LIMDEP,apackageoriginallydevelopedfortheanalysis of a certain class of cross-sectional data, has many useful features for mod-elling financial time series. Moreover, many packages developed for time seriesanalysis,suchasTSP(‘TimeSeriesProcessor’),canalsonowbeusedfor cross-sectional or panel data. Of course, this choice may be made for you if your institutionoffers or supports only one or two of the above possibilities. Otherwise, sensible questions to ask yourself are as follows. ● Is the package suitable for your intended applications – for example, does thesoftwarehavethecapabilityforthemodelsthatyouwanttoestimate? Can it handle sufficiently large databases? ● Is the package user-friendly? ● Is it fast? ● How much does it cost? Introduction 15 ● Is it accurate? ● Is the package discussed or supported in a standard textbook? ● Doesthepackagehavereadableandcomprehensivemanuals?Ishelpavailable online? ● Does the package come with free technical support so that you can e-mail the developers with queries? A great deal of useful information can be obtained most easily from the web pages of the software developers. Additionally, many journals (includ-ing the Journal of Applied Econometrics, the Economic Journal, the International Journal of Forecasting and the American Statistician) publish software reviews that seek to evaluate and compare the packages’ usefulness for a given pur-pose. Three reviews that the first author has been involved with are Brooks (1997) and Brooks, Burke and Persand (2001, 2003). 1.10 Outline of the remainder of this book Chapter 2 Thischapteraimstoillustratedatatransformationandcomputation,which are key to the construction of real estate series. The chapter also provides the mathematical foundations that are important for the computation of statistical tests in the following chapters. It begins by looking at how to index a single data series and produce a composite index from several series by different methods. The chapter continues by showing how to convert nominal data into real terms. The discussion explains why we log data and reminds the reader of the properties of logs. The calculation of simple and continuously compounded returns follows, a topic of much relevance in the construction of real estate series such as capital value (or price) and total returns. The last section of the chapter is devoted to matrix alge-bra. Key aspects of matrices are presented for the reader to help his/her understanding of the econometric concepts employed in the following chapters. Chapter 3 This begins with a description of the types of data that may be available for the econometric analysis of real estate markets and explains the concepts of time series, cross-sectional and panel data. The discussion extends to the properties of cardinal, ordinal and nominal numbers. This chapter covers important statistical properties of data: measures of central tendency, such asthemedianandthearithmeticandgeometricmeans;measuresofspread, ... - tailieumienphi.vn