Property Market Modelling and Forecasting: a Case for Simplicity Arvydas Jadevicius PhD Candidate Edinburgh Napier University 2013.

Property Market Modelling and Forecasting: a Case for SimplicityArvydas JadeviciusPhD CandidateEdinburgh Napier University2013

Real Estate Models

Figure 1. Forecasting MethodsSource: Lizieri (2009)

Simple vs Complex models

VSa) Simple model b) Complex model

• business, management, economics, environment, real estate, physiology;

Various fields of research

• uncomplicated combination of rules, limited number of variables, fixed structure, atheoretical;

Simple models

• newer, adaptive, include more variables, accounts for attributes of external environment.

Complex models

What is the difference?

Simple >

Complex

Business Literature

Real Estate Literature

Other studies

Which ones are better?

Opposite findings: Armstrong (1975),

Pandy (2003) and Li et.al. (2005)

Initial modelling resultsModel Specification R-squared MAE MAPE AIC Theil’s USimple Exponential Smoothing -0.03 4.40 109.26 130.87 0.94Holt’s Linear Trend -0.03 4.39 110.32 131.15 0.93Brown’s Linear Trend -0.00 4.36 100.24 131.23 1.00Simple Regression (Bank Rate) 0.00 4.33 98.26 130.47 0.95Simple Regression (Construction Costs) 0.00 4.36 98.86 130.49 0.97Simple Regression (Construction Orders) 0.34 3.74 142.38 115.26 0.41Simple Regression (Construction Output) 0.02 4.53 108.08 129.94 0.88Simple Regression (Construction Starts) 0.00 4.35 97.93 130.46 0.93Simple Regression (Employment) 0.03 4.09 87.64 129.36 0.82Simple Regression (GDP) 0.32 3.63 120.19 118.50 0.47Multiple Regression 0.55 3.06 141.65 109.35 0.46Vector Autoregression 0.79 2.47 91.85 85.180 0.48ARIMA (1,0,2) 0.52 2.60 66.04 109.85 0.85ARIMAX (1,0,2) (Bank Rate) 0.52 2.58 66.40 112.32 0.82ARIMAX (1,0,2) (Construction Costs) 0.52 2.61 66.69 112.46 0.74ARIMAX (1,0,2) (Construction Orders) 0.60 2.61 80.83 103.24 0.33ARIMAX (1,0,2) (Construction Output) 0.52 2.60 65.49 112.66 0.84ARIMAX (1,0,2) (Construction Starts) 0.52 2.60 67.45 112.27 0.83ARIMAX (1,0,2) (Employment) 0.52 2.60 66.65 112.90 0.84ARIMAX (4,0,0) (GDP) 0.69 2.26 69.83 99.09 0.43

Initial modelling resultsModel Specification R-squared MAE MAPE AIC Theil’s USimple Exponential Smoothing -0.03 4.40 109.26 130.87 0.94Holt’s Linear Trend -0.03 4.39 110.32 131.15 0.93Brown’s Linear Trend -0.00 4.36 100.24 131.23 1.00Simple Regression (Bank Rate) 0.00 4.33 98.26 130.47 0.95Simple Regression (Construction Costs) 0.00 4.36 98.86 130.49 0.97Simple Regression (Construction Orders) 0.34 3.74 142.38 115.26 0.41Simple Regression (Construction Output) 0.02 4.53 108.08 129.94 0.88Simple Regression (Construction Starts) 0.00 4.35 97.93 130.46 0.93Simple Regression (Employment) 0.03 4.09 87.64 129.36 0.82Simple Regression (GDP) 0.32 3.63 120.19 118.50 0.47Multiple Regression 0.55 3.06 141.65 109.35 0.46Vector Autoregression 0.79 2.47 91.85 85.180 0.48ARIMA (1,0,2) 0.52 2.60 66.04 109.85 0.85ARIMAX (1,0,2) (Bank Rate) 0.52 2.58 66.40 112.32 0.82ARIMAX (1,0,2) (Construction Costs) 0.52 2.61 66.69 112.46 0.74ARIMAX (1,0,2) (Construction Orders) 0.60 2.61 80.83 103.24 0.33ARIMAX (1,0,2) (Construction Output) 0.52 2.60 65.49 112.66 0.84ARIMAX (1,0,2) (Construction Starts) 0.52 2.60 67.45 112.27 0.83ARIMAX (1,0,2) (Employment) 0.52 2.60 66.65 112.90 0.84ARIMAX (4,0,0) (GDP) 0.69 2.26 69.83 99.09 0.43

Initial modelling resultsModel Specification R-squared MAE MAPE AIC Theil’s USimple Exponential Smoothing -0.03 4.40 109.26 130.87 0.94Holt’s Linear Trend -0.03 4.39 110.32 131.15 0.93Brown’s Linear Trend -0.00 4.36 100.24 131.23 1.00Simple Regression (Bank Rate) 0.00 4.33 98.26 130.47 0.95Simple Regression (Construction Costs) 0.00 4.36 98.86 130.49 0.97Simple Regression (Construction Orders) 0.34 3.74 142.38 115.26 0.41Simple Regression (Construction Output) 0.02 4.53 108.08 129.94 0.88Simple Regression (Construction Starts) 0.00 4.35 97.93 130.46 0.93Simple Regression (Employment) 0.03 4.09 87.64 129.36 0.82Simple Regression (GDP) 0.32 3.63 120.19 118.50 0.47Multiple Regression 0.55 3.06 141.65 109.35 0.46Vector Autoregression 0.79 2.47 91.85 85.180 0.48ARIMA (1,0,2) 0.52 2.60 66.04 109.85 0.85ARIMAX (1,0,2) (Bank Rate) 0.52 2.58 66.40 112.32 0.82ARIMAX (1,0,2) (Construction Costs) 0.52 2.61 66.69 112.46 0.74ARIMAX (1,0,2) (Construction Orders) 0.60 2.61 80.83 103.24 0.33ARIMAX (1,0,2) (Construction Output) 0.52 2.60 65.49 112.66 0.84ARIMAX (1,0,2) (Construction Starts) 0.52 2.60 67.45 112.27 0.83ARIMAX (1,0,2) (Employment) 0.52 2.60 66.65 112.90 0.84ARIMAX (4,0,0) (GDP) 0.69 2.26 69.83 99.09 0.43

GVA (2009):GDP & Construction Orders

What does it tell?• similar outcomes can be achieved from

different models, i.e equifinality (Byrne et.al., 2010);

Overall findings

• make forecasts more user-friendly;• develop and improve simpler forecasting

techniques and/or simplify more complex structures;

Simplicity in modelling

• Keep it Sensibly/Sophisticatedly Simple (Zellner, 1991; Kennedy, 2002).KISS

“Complexity gives an illusion of control” Lord McFall BBC Radio 5 live, Wake Up To Money19 June 2013

Lecturer in Real EstateSchool of Real Estate and Land ManagementRoyal Agricultural UniversityCirencester GL7 6JSUnited Kingdom