Real estate growth and carbon emissions control. An analysis of challenges to reach 2050 . CRREM PROJECT Paloma Taltavull de La Paz Francisco Juárez Raul Pérez Sánchez Universidad deAlicante Pacific Rim Real Estate Conference PRRES 2019 Melbourne,Australia This project has received funding from the European 1 Union’s Horizon 2020 research andinnovation programme under grant agreement no. 785058 .
Agenda • Introduction to CRREM. • Tool to estimate how much energy should be saved to fulfill energy efficiency goals 2050 • What this paper does and aim • Model estrategy and steps • Results • Conclusions This project has received funding from the European 2 Union’s Horizon 2020 research andinnovation programme under grant agreement no. 785058 .
What is CRREM • Carbon Risk Real Estate Monitor – Project 785058 of H2020 EU program, Energy topic • Main goal: to estimate the required investment in the existing comercial building in order to improve their energy efficiency and reduce carbon emissions – Speed of energy renovation the building stock should follow – Identify the stranded assets ….Stranding risk • Build a tool to allow estimating to particular carbon efforts: – Real estate assets – Portfolios – Aggregate This project has received funding from the European 3 Union’s Horizon 2020 research andinnovation programme under grant agreement no. 785058 .
The idea This project has received funding from the European 4 Union’s Horizon 2020 research andinnovation programme under grant agreement no. 785058 .
The idea • Not so simple: – At national level in EU (28!!) – At portfolio level – At building/asset type level • And.. – Climate becomes + hot This project has received funding from the European 5 Union’s Horizon 2020 research andinnovation programme under grant agreement no. 785058 .
What is CRREM • Consortium: 5 partners – Coordinator: IIÖ (research centre), Austria – GRESB, The Netherlands – University of Tilburg – University of Ulster – University of Alicante • Strong links with companies (investor and energy oriented firms) – EIC organization • http://crrem.eu This project has received funding from the European 6 Union’s Horizon 2020 research andinnovation programme under grant agreement no. 785058 .
What is CRREM • Calculation methodology follows several steps: – Data base construction – Estimate the carbon impact of retrofitting in emissions and monetary investment – Fit how emissions evolve with the carbon target – Calculate the future increase on emissions • Forecast the future building trend – All affect the emissions stream: horizon 2050 – All follow process of VERIFICATION of data and results This project has received funding from the European 7 Union’s Horizon 2020 research andinnovation programme under grant agreement no. 785058 .
Step covered by this paper: Forecast building activity • Public forecasting are incompleted for our needs Source: EUREF16, in wp2 report, This project has received funding from the European 8 Union’s Horizon 2020 research andinnovation programme under grant agreement no. 785058 .
Aim of this presentation • Show the forecasting strategy: 2018-2050! – Time series arena: VAR environment methodology – Long term series are needed – Yearly data – Forecasting construction (m2) is needed… • Supply side model – Estochastic modelling • We cannot advance any innovation nor structural change – We can estimate the growth trend in the future done the past knowledge. This project has received funding from the European 9 Union’s Horizon 2020 research andinnovation programme under grant agreement no. 785058 .
Conceptual model • Di Pasquale & Wheaton (1996) shows that new supply space reacts to changes on market prices and construction costs. 2 C t 3 I t 4 S t-1 5 Qre ts = f(P re,t , Cc t ,S t-1 , G tk ) = e 1 P re,t [G tk ] 6 t – where: – P re,t corresponds to real estate prices in real terms (market prices not developer prices) – Cc t corresponds to the costs associated with construction materials and labor – i t reflects the real interest rates paid by developers for building credits – S t-1 is the existing stock at the previous moment – G tk is a matrix of the regional market characteristics, including physical features as well as other aspects like land and market size t is a random term 1..6 are the estimatedparameters. This project has received funding from the European 10 Union’s Horizon 2020 research andinnovation programme under grant agreement no. 785058 .
Problems? • Long term data is not available enough. – Time series comes from 1990 (quarterly) but prices from 2005! • Yearly base forecasting is better (less estimated points than quarterly and with no seasonal effects) but requires long term evidence. • Proxies? • Forecasting method. – Ideal: stochastic – Deterministic This project has received funding from the European 11 Union’s Horizon 2020 research andinnovation programme under grant agreement no. 785058 .
Econometric Strategy The analytical process follows the conventional sequential steps: • 1.- stationary analysis, • 2.- VAR definition and lag structure analysis, • 3.- Cointegration tests identification, • 4.- VECM definition, diagnosis and final model estimation, • 5.- Forecasting. • Separate country to country This project has received funding from the European 12 Union’s Horizon 2020 research andinnovation programme under grant agreement no. 785058 .
Econometric Strategy • Different methods for forecasting • 1 st . Looking for a proxies: housing prices? – Prices prediction other prices?. Evidence – Exogenous prove, using GDP • 2 nd . Forecasting with proxies – Supply model for commercial building permits • Offices • Commercial real estate (no-offices sector) This project has received funding from the European 13 Union’s Horizon 2020 research andinnovation programme under grant agreement no. 785058 .
Econometric Strategy Two steps for forecast: 1 st .- In-sample period [T-q+1,T], with q<T, in order to choose the model which minimize theprediction errors to fit available data. Estimated with fixed estimated regressors (following Pesaran and Timmermann,2005). • • These models have been using for prediction in real estate and construction by Jiang and Liu (2011), Kouwenberg and Zwinkels (2014) or Bork and Moller(2015). 2 nd Out-of-sample data until2050 • ‘ the expanding window strategy’ (Pesaran and Timmermann, 2007) through which a [T+m] future periods are estimated (with m>T) using a dynamic-stochastic simulation (Broyden 1969 solver) to calculate the future values in m-Tphases. – Every model is repeated 1000 times allowing to a 5000 maximum iterations. When the idiosyncratic features in the estimation process requires using more than one method of forecasting, utilizing the so-called as ‘the predictors technique ’ (Clements and Hendry, 1995) • i.e. by including a variable as a predictor which can be demonstrated to have strong propertiesto approach the variable of interest. A combination forecast would be also applied to obtain consistent out of-sample predictions (Timmermann, 2006, Aiolfi et al, 2010 among othercontributions). This project has received funding from the European 14 Union’s Horizon 2020 research andinnovation programme under grant agreement no. 785058 .
1 st stage: housing prices model This project has received funding from the European 15 Union’s Horizon 2020 research andinnovation programme under grant agreement no. 785058 .
1 st . Housing price model • Dynamics seems to follow similar (lagged) cycle • Supporting the idea of co-movements and the availability to be used as proxy (with the correct lag) Figure 5.1.5-Prices: housing and offices Figure 5.1.6. Starts of housing and offices(m2) 2,400 3 Linearscaling Normaliseddata 2,000 2 1,600 1 1,200 0 800 -1 400 Sources: M F O M andA N C E R T 0 -2 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 1975 1980 1985 1990 1995 2000 2005 2010 2015 P _ H pof_m2 OFIC_M2 S T A R T S This project has received funding from the European 16 Union’s Horizon 2020 research andinnovation programme under grant agreement no. 785058 .
1 st . Housing price model Meen(2001): P hdt = f(X, Z) t = 2 (Pop) t + 3 (y) t + 4 (K) t – 5 (Dh) t – 6 (Cu) t + t • Table 1. Variables in Model 1 of Housing Prices Available Variable Concept period Source MFOM, Dallas Fed and Taltav Phdt Housing prices by m2 - Ph 1971-2018 and Juárez 2015 population older than 20 years. Taken in differences Pop Pob>20 1971-2018 INE Y GDP real terms -RGDP 1971-2018 INE Finance flow to buy houses (number of mortgages to K buy a house)- FF 1971-2018 INE, mortgage statistics Changes in the stock measured by the flow of starts h STARTS 1971-2018 MFOM User costs, measured by interest rates (real) and Cu inflation, RIRM, INF 1971-2018 Bank of Spain, INE This project has received funding from the European 17 Union’s Horizon 2020 research andinnovation programme under grant agreement no. 785058 .
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