sentiment-based commercial real estate forecasting with google search volume data

Sentiment-Based Commercial Real Estate Forecasting with Google Search Volume DataMarian Alexander Dietzel | Nicole Braun | Wolfgang SchaefersERES Conference Bucharest 2014

2

Textmasterformate durch Klicken bearbeitenSentiment-Based Commercial Real Estate Forecasting with Google Search Volume Data

AGENDA

1. Motivation and Theoretical Background

2. Research Design and Methodology

2.1. Data

2.2. Models

4. Empirical Results

5. Conclusion

3

Textmasterformate durch Klicken bearbeiten

Beracha, E. and Wintoki, J. (2012), “Predicting Future Home Price Changes Using Current Google Search Data,” Journal of Real Estate Research, forthcoming.

Hohenstatt, R., Käsbauer, M. and Schäfers, W. (2011), “’Geco’ and its Potential for Real Estate Research: Evidence from the U.S. Housing Market”, Journal of Real Estate Research, Vol. 33 No. 4., pp. 471-506.

Hohenstatt, R. and Käsbauer, M. (2013), “GECO’s Weather Forecast’ for the U.K. Housing Market: To What Extent Can We Rely on Google ECOnometrics?”, Journal of Real Estate Research, forthcoming.

Wu, L. and Brynjolfsson, E. (2009), “The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales”, Working papers, Wharton School, University of Pennsylvania

Housing Market Predictions with Google Trends Data

Motivation and Theoretical Background

All studies find empirical evidence that Google Trends data have predictive power and improve the forecast accuracy for housing markets (USA and UK).

4



Can Google Trends data also improve Commercial Real Estate Market Forecasts?

Research Question

5

Textmasterformate durch Klicken bearbeitenGraphical Inspection in annual differences


CoStar Composite Index

Google

6

Textmasterformate durch Klicken bearbeitenGraphical Inspection in annual differences


CoStar Composite transactions

Google

7


• unspecific: internet search for market/investment climate and comparables (yields, rents etc.)

• listing services • real estate agents/property news websites

• specific: internet search for market/investment

climate and comparables (yields, rents etc.)

• comparison of other analyses

internet search for actual properties

• listing services • real estate agents (JLL, CBRE etc.)

setting of initial investment goals and decision criteria

formulation of investor specific strategy

Stage 1

formulation of a decision-making strategy

choosing rational criteria for asset selection

Stage 2

search for suitable properties

detailed search for alternative investment opportunities

Stage 3

prediction of outcomes

investment appraisal

Stage 5

information input (analysis of market conditions)

analysis of economic, political and investment climate for national and regional

markets

Stage 4

market related

interest

ob

jec

t rela

ted

inte

res

t:

Stage 6: application of decision criteria; Stage 7: trade-off between properties; Stage 8: project screening; Stage 9: investment selection; Stage 10: deal resolution and post investment activity

Transaction Process and Internet Research


Investment Process after Roberts and Henneberry (2007)

8

Textmasterformate durch Klicken bearbeitenGoogle Data

Research Design and Methodology

Search Volume Indices (SVI) derived from Google Trends (http://www.google.com/trends/)

Normalized values, scaled measured between 0 and 100

The weekly data covers search queries conducted from Sunday to Saturday.

Google Trends makes the newest weekly data available with an approximate two day delay.

http://www.google.com/trends/

http://www.google.com/trends/

9

Textmasterformate durch Klicken bearbeitenMacro Data


Commercial Real Estate Data:

CoStar Commercial Repeat-Sale Indices CCRSI

Moody‘s/RCA Commercial Property Price Indices CPPI

Macroeconomic Data:

US unemployment initial claims

US construction expenditures

National Financial Conditions Index (NFCI)

Chicago Fed National Activity Index (CFNAI)

10


Google Index Search Interest Search Terms

general interest

g_inv_subcat"Commercial and Investment Real Estate" subcategory

Google does not report the exact search terms that were aggregated in the subcategory

g_commGeneral search terms for commercial real estate

commercial property+commercial real estate+commercial property sale+property for sale+lease commercial

property+commercial lease

g_agents+listCommercial real estate service providers and listing services

jll+cbre+jones lang lasalle+ colliers+dtz+ cushman and wakefield +knight frank+savills+grubb ellis+newmark

grubb+cb richard ellis+marcus millichap+cimls+loopnet+xceligent+

"propertyline"+catylist

specific interest

g_off Office property related search termsoffice for sale+office space+office space rent+commercial

office space+office rental+office lease

g_ret Retail property related search termsretail space+commercial retail+retail lease+retail sale

space+retail property+rent retail space+retail space for sale

g_indus Industrial-property- related search termsindustrial property+industrial for sale+industrial leases+commercial industrial property+industrial

building+warehouse for sale+industrial property for sale

g_apart "Apartments & Residential Rentals" subcategory

Google does not report the exact search terms that were aggregated in the subcategory

Google Data


11

Textmasterformate durch Klicken bearbeitenModels


model forecasted variable independent variables

Macro Data Transactions Google Data

Prices

baselineb1 Prices x

b2 Prices x x

googleg1 Prices x

g2 Prices x x x

Macro Data Prices Google Data

Transactio

ns

baselineb1 Transactions x

b2 Transactions x x

googleg1 Transactions x

g2 Transactions x x x

12

Textmasterformate durch Klicken bearbeitenModel Specification


g2(Pr Tr) Τ :ۉ

ۈۇ

𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛 1:𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛 2:𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛 3:𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛 4:𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛 5: ی

ۋ ۊ

ۉ

ۈۇ

𝑃𝑡𝑇𝑡𝐺1𝑡𝐺2𝑡𝐺3𝑡 ی

ۋ =ۊ

ۉ

ۈۇ

𝛽01𝛽02𝛽03𝛽04𝛽05 ی

ۋ+ۊ 𝐴

ۉ

ۈۇ

𝑃𝑡−𝑖𝑇𝑡−𝑖𝐺1𝑡−𝑖𝐺2𝑡−𝑖𝐺3𝑡−𝑖 ی

ۋ+ۊ 𝐵 ൮𝐶𝐹𝑁𝐴𝐼𝑡𝐶𝑜𝑛𝑠𝑡𝑡𝑁𝐹𝐶𝐼𝑡𝑈𝑛𝑒𝑚𝑝𝑡

൲+ۉ

ۈۇ

𝑢𝑃𝑡𝑢𝑇𝑡𝑢𝐺1𝑡𝑢𝐺2𝑡𝑢𝐺3𝑡 ی

ۋ ۊ

VAR (6)-Model

endogenous variables

exogenous

variables

13

Textmasterformate durch Klicken bearbeitenPrice Forecasts

Empirical Results

Dependent Variable MacroTransactions

/ Prices Google Adj. R² MSEMSE

Reduction * U1 Theil *

b1 x 0.461 4.1108 -12% 0.062

b2 x x 0.473 3.6868 0% 0.059

g1 x 0.515 2.7364 26% 0.051

g2 x x x 0.484 2.4670 33% 0.048

b1 x 0.498 3.7666 -15% 0.059

b2 x x 0.526 3.2691 0% 0.055

g1 x 0.568 2.3054 29% 0.046

g2 x x x 0.561 1.9800 39% 0.043

b1 x 0.538 10.8856 -13% 0.080

b2 x x 0.559 9.5989 0% 0.076

g1 x 0.641 6.1344 36% 0.060

g2 x x x 0.665 4.4380 54% 0.052

google

co_inv

baseline

google

co_comp

baseline

co_gen

baseline

google

Prices

* The reduction of the MSE and U1 Theil is always measured in relation to the best baseline model (usually b2). A positive value stands for an improvement in forecasting accuracy in comparison to the baseline model.

14

Textmasterformate durch Klicken bearbeitenTransaction Forecasts

Empirical Results




b1 x 0.276 0.0024 -31% 0.210

b2 x x 0.392 0.0018 0% 0.188

g1 x 0.386 0.0016 10% 0.170

g2 x x x 0.448 0.0013 29% 0.155

b1 x 0.304 0.0021 -23% 0.177

b2 x x 0.386 0.0017 0% 0.164

g1 x 0.421 0.0014 16% 0.143

g2 x x x 0.474 0.0011 33% 0.132

b1 x 0.343 0.0074 -4% 0.191

b2 x x 0.306 0.0072 0% 0.188

g1 x 0.451 0.0052 28% 0.155

g2 x x x 0.466 0.0047 35% 0.149

google

co_inv

baseline

google

co_comp

baseline

co_gen

baseline

google

Transactions


15

Textmasterformate durch Klicken bearbeitenRobustness across Real Estate Sectors

Robustness Check




b1 x 0.852 2.5167 -23% 0.027

b2 x x 0.844 2.0522 0% 0.025

g1 x 0.880 1.9803 4% 0.024

g2 x x x 0.864 1.3859 32% 0.020

b1 x 0.204 8.7869 -31% 0.071

b2 x x 0.314 6.7246 0% 0.062

g1 x 0.196 7.2237 -7% 0.065

g2 x x x 0.380 4.5788 32% 0.051

b1 x 0.766 1.1233 -34% 0.028

b2 x x 0.796 0.8397 0% 0.024

g1 x 0.754 0.9747 -16% 0.026

g2 x x x 0.816 0.6001 29% 0.020

b1 x 0.891 0.9711 -16% 0.019

b2 x x 0.894 0.8375 0% 0.018

g1 x 0.902 0.7271 13% 0.016

g2 x x x 0.914 0.4731 44% 0.013

mo_indus

baseline

multifamily mo_apart

baseline

office

google

google

industrial

retail mo_ret

baseline

mo_off

google

google

baseline

Prices


16

Textmasterformate durch Klicken bearbeitenRobustness across Real Estate Sectors

Robustness Check




b1 x 0,290 0,0242 -17% 0,287

b2 x x 0,340 0,0206 0% 0,260

g1 x 0,304 0,0188 9% 0,246

g2 x x x 0,387 0,0152 26% 0,220

b1 x 0,355 0,0365 -23% 0,378

b2 x x 0,408 0,0296 0% 0,335

g1 x 0,424 0,0265 10% 0,305

g2 x x x 0,451 0,0223 25% 0,279

b1 x 0,357 0,0253 -20% 0,313

b2 x x 0,407 0,0211 0% 0,277

g1 x 0,368 0,0202 4% 0,267

g2 x x x 0,416 0,0168 20% 0,242

b1 x 0,397 0,0071 -25% 0,188

b2 x x 0,452 0,0057 0% 0,165

g1 x 0,456 0,0048 16% 0,151

g2 x x x 0,485 0,0040 31% 0,136

Transactions

google

google

baseline

office

google

google

industrial

retail mo_ret

baseline

mo_off

mo_indus

baseline

multifamily mo_apart

baseline


17

Textmasterformate durch Klicken bearbeitenClark-West Forecast Improvement Significance Tests

Robustness Check

t-stat p-value t-stat p-valueco_comp 5,031109 0,0000 *** 4,566811 0,0000 ***co_gen 5,161966 0,0000 *** 4,946523 0,0000 ***co_inv 4,512639 0,0000 *** 4,299512 0,0001 ***

office mo_off 3,838397 0,0003 *** 3,030389 0,0034 ***

retail mo_ret 4,35522 0,0000 *** 4,247521 0,0001 ***

industrial mo_indus 5,114553 0,0000 *** 3,311156 0,0015 ***

multifamily mo_apart 3,2297 0,0019 *** 3,926208 0,0002 ***

Notes: H=0: improvement of g2 over b2 model is insignificant. Sample: 2007m01 2013m01; 73 observationsSignificant at: * p < 0.10, ** p < 0.05 and *** p < 0.01.

all property

Prices Transactions

18

Textmasterformate durch Klicken bearbeitenMain Findings

Findings and Conclusion

Google data help in improving the forecast accuracy for the commercial real

estate market

g2-models have the lowest mean squared forecast errors

a combination of macro and Google data yields the best forecasting results

Models based on Google data only outperform non-Google models in most cases

(78%)

Google data by itself has significant explanatory power towards the

commercial real estate market

19

Textmasterformate durch Klicken bearbeitenSentiment-Based Commercial Real Estate Forecasting with Google Search Volume Data

Questions

Thank you for your attention!

Questions?

20

Textmasterformate durch Klicken bearbeitenGranger Causality Test

Prices g_agents_list g_comm g_inv_subcat Transactions g_agents_list g_comm g_inv_subcat

d ** *** ** d **/+ ** ***/++i **/++ */++ i ***/+++ **d * ** *** d ***/++ */++ **/++i + ++ */++ i **/+++ *d ** ***/++ d *** **/++i ** ++ id d */+i ***/+++ i *d d * ** *i **/+ i *

g_agents_list g_comm g_off g_agents_list g_comm g_off

d **/+ di i ***d ** *** d **/++i id *** di i ***

g_agents_list g_comm g_ret g_agents_list g_comm g_ret

d d * ***/+i ***/+++ i **

g_agents_list g_comm g_indus g_agents_list g_comm g_indus

d ***/+ d *i + + i *

g_agents_list g_comm g_apartrent g_agents_list g_comm g_apartrent

d *** d *i + i ++ **/++

Cluster 5 mo_apart Cluster 5 mo_apart_tra

Cluster 3 mo_ret Cluster 3 mo_ret_tra

Cluster 4 mo_indus Cluster 4 mo_indus_tra

Cluster 1

co_gen

Cluster 1

co_gen_tra

co_comp co_comp_tra

co_inv co_inv_tra

mo_alprop mo_alprop_tra

mo_core mo_core_tra

mo_off_cbd mo_off_cbd_tra

mo_off_sub mo_off_sub_tra

Cluster 2 Cluster 2

mo_off mo_off_tra

BACK UP

21


Prices RE Indices Transactions RE Indices

g_agents_list g_comm g_inv_subcat g_agents_list g_comm g_inv_subcatco_gen 7 7 7 co_gen_tra 4 5 3co_comp 7 7 7 co_comp_tra 4 4 3co_inv 7 7 7 co_inv_tra 4 3 2mo_alprop 5 6 5 mo_alprop_tra 4 2 3mo_core 5 6 5 mo_core_tra 5 6 2

g_agents_list g_comm g_off g_agents_list g_comm g_offmo_off 6 6 6 mo_off_tra 4 3 2mo_off_cbd 7 6 6 mo_off_cbd_tra 2 1 2mo_off_sub 4 2 2 mo_off_sub_tra 3 2 2

g_agents_list g_comm g_ret g_agents_list g_comm g_retmo_ret 7 3 3 mo_ret_tra 3 3 8

g_agents_list g_comm g_indus g_agents_list g_comm g_indusmo_indus 2 7 5 mo_indus_tra 6 1 7

g_agents_list g_comm g_apartrent g_agents_list g_comm g_apartrentmo_apart 8 6 6 mo_apart_tra 5 2 6Cluster 5

Google SVI

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 5

Cluster 4

Google SVI

Cluster 1

Cluster 2

Cluster 3

Lag Order

BACK UP

22

Textmasterformate durch Klicken bearbeitenClark-West Forecast Significance Test

BACK UP

Prices t-stat p-value Transaction

s t-stat p-value

Cluster 1

co_comp 5.031109 0.0000***

Cluster 1

co_comp_tra 4.566811 0.0000***co_gen 5.161966 0.0000*** co_gen_tra 4.946523 0.0000***co_inv 4.512639 0.0000*** co_inv_tra 4.299512 0.0001***

mo_alprop 3.453596 0.0009*** mo_alprop_tra 3.19397 0.0021***

mo_core 3.341429 0.0013*** mo_core_tra 2.690448 0.0089***

Cluster 2

mo_off 3.838397 0.0003***

Cluster 2

mo_off_tra 3.030389 0.0034***

mo_off_cbd 3.204947 0.0020*** mo_off_cbd_tra 3.181805 0.0022***

mo_off_sub 3.653564 0.0005*** mo_off_sub_tra 3.399589 0.0011***

Cluster 3 mo_ret 4.35522 0.0000*** Cluster 3 mo_ret_tra 4.247521 0.0001*** Cluster 4 mo_indus 5.114553 0.0000*** Cluster 4 mo_indus_tra 3.311156 0.0015*** Cluster 5 mo_apart 3.2297 0.0019*** Cluster 5 mo_apart_tra 3.926208 0.0002***

Notes: H=0: improvement of g2 over b2 model is insignificant. Sample: 2007m01 2013m01; 73 observationsSignificant at: * p < 0.10, ** p < 0.05 and *** p < 0.01.

23

Textmasterformate durch Klicken bearbeitenImpulse Response Function

BACK UP

PricesDependent

Variable Google SVI Transaction

sDependent

Variable Google SVI

g_agents_lis

t g_inv_subcat g_comm g_agents_lis

t g_inv_subcat g_comm

Cluster 1

co_gen 0.061936 0.176108 1.64041

Cluster 1

co_gen_tra 0.006833 0.008523 0.005798

co_comp 0.246844 -0.439046 2.019921 co_comp_tra 0.009521 0.009725 0.002869

co_inv 4.822633 -0.580048-

2.831145 co_inv_tra 0.000393 0.014699-

0.001062

mo_alprop 0.50313 0.364071 0.820831 mo_alprop_tra 0.020587 0.027219 0.031579

mo_core 0.700351 0.119554 0.510219 mo_core_tra 0.025052 0.035246 0.042196

g_agents_list g_inv_subcat g_off

g_agents_list g_inv_subcat g_off

Cluster 2

mo_off 0.492695 0.487502 0.167846

Cluster 2

mo_off_tra 0.007282 0.016193 0.002459

mo_off_cbd -0.642707 3.146042-

1.066668 mo_off_cbd_tra 0.003894 0.021436-

0.024235

mo_off_sub 0.293834 0.225726 0.936703 mo_off_sub_tra 0.000949 0.034404 0.002516

g_agents_list g_inv_subcat g_ret

g_agents_list g_inv_subcat g_ret

Cluster 3 mo_ret 1.604485 0.514974 0.15208 Cluster 3 mo_ret_tra 0.036414 0.073457 0.001075

g_agents_list g_inv_subcat g_indus

g_agents_list g_inv_subcat g_indus

Cluster 4 mo_indus -0.41082 0.973099 0.095609 Cluster 4 mo_indus_tra 0.035558 0.012609 0.006899

g_agents_list g_inv_subcat

g_apartrent

g_agents_list g_inv_subcat

g_apartrent

Cluster 5 mo_apart 0.737968 0.408705-

0.052707 Cluster 5 mo_apart_tra 0.000585 0.010047-

0.012387

24

Textmasterformate durch Klicken bearbeitenMacro Data

Transactions

co_comp_tra Transactions underlying CoStar Composite CoStar

co_gen_tra Transactions underlying CoStar General Commercial CoStar

co_inv_tra Transactions underlying CoStar Investment Grade CoStar

mo_alprop_tra Transactions underlying Moody's/RCA Tier 1 National Moody's/RCA

mo_core_traTransactions underlying Moody's/RCA Tier 2 Core Commercial Moody's/RCA

mo_off_tra Transactions underlying Moody's/RCA Tier 4 Office Moody's/RCA

mo_off_cbd_tra Transactions underlying Moody's/RCA Tier 4 Office CBD Moody's/RCA

mo_off_sub_traTransactions underlying Moody's/RCA Tier 4 Office Suburban Markets Moody's/RCA

mo_ret_tra Transactions underlying Moody's/RCA Tier 4 Retail Moody's/RCA

mo_indus_tra Transactions underlying Moody's/RCA Tier 4 Industrial Moody's/RCA

mo_apart_tra Transactions underlying Moody's/RCA Tier 2 Apartment Moody's/RCA

Macro Data

mcr_const Construction Expenditures US Census Bureau

mcr_unempU.S Labor Department report of initial state jobless benefit claims

US Department of Labor

mcr_nfci National Financial Conditions Index (NFCI)**Federal Reserve Bank of Chicago

mcr_cfnai

Chicago Fed National Activity Index (CFNAI), a monthly index designed to gauge overall economic activity and related inflationary pressure ***

Federal Reserve Bank of Chicago

BACK UP

sentiment-based commercial real estate forecasting with google search volume data

Documents