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- 2.5 Final Merged Data Our analysis depends on measuring both nominal losses and home equity. This imposes some restrictions on the sample. We have transactions data available from 1992 to the present, meaning that we can only measure the purchase price of properties that were bought during or after 1992. Moreover, the mortgage data run from 2009 to 2016. In addition, the sample is restricted to properties for which we know both the ID of the owner, as well as that of the owner’s household, in order to match with demographic information. For listings that end in a final sale, we drop within-household transactions and transactions that Statistics Denmark flag as anomalous or unusual. We flag (but do not drop) listings by households that do not have a stable structure, that is, we create a dummy for those listings for which the household ceases to exist as a unit in the year following the listing owing to death or divorce. We also flag households with missing education information.
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- Final sample size 217,028 216,960 27,311 -42,373 40,144 Estimation sample 153,256 153,228 25,967 -153,256 150,268 R 2 0.8606 0.867 0.9709 -0.3579 0.52 Full sample estimation (1992-2016) Timeinvariant property characteristics X X X X Timevariant property characteristics X X Property -size interactions X Municipality -sales year fixed effects X X X X X Municipality -purchase year fixed effects X Shire -sales year fixed effects -X Property fixed effects X X
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- OLS 2SLS (1) (2) (3) (4) Single IV Overidentified Demand concavity-0.384∗∗∗ -0.479∗∗∗ -0.465∗∗∗ -0.468∗ (0.071) (0.121) (0.083) (0.267) Household controls X Observations 95 95 95 95 R2 0.396 First-stage F-stat 37.126 36.153 25.376 Hansen J-stat (p-val) 0.222 0.912 Reference Dependence in the Housing Market Online Appendix (For online publication) Steffen Andersen∗ Cristian Badarinza†Lu Liu‡ Julie Marx Tarun Ramadorai November 20, 2019 ∗ Copenhagen Business School and CEPR, Email: san.fi@cbs.dk. †National University of Singapore, Email: cristian.badarinza@nus.edu.sg ‡ Imperial College London, Email: l.liu16@imperial.ac.uk. Copenhagen Business School, Email: jma.fi@cbs.dk. Corresponding author: Imperial College London, Tanaka Building, South Kensington Campus, London SW7 2AZ, and CEPR. Tel.: +44 207 594 99 10. Email: t.ramadorai@imperial.ac.uk.
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- To estimate the change in listing premium slope across gains, we choose V = G as the assignment variable, and v = 0 as the kink point. To estimate the effect of demand concavity, V = `, with a baseline kink threshold of v = 0%. Table A.5 reports results across bandwidths b ∈ {b∗ , 15, 20} around each of the running variables. b∗ denotes the mean-squared-error optimally chosen bandwidth following Calonico et al (2014) and we use a polynomial order p = 2 for gains, and p = 1 for demand concavity.12 Figures A.16 12 The precision but not the size of the estimate for unconstrained households depends on the use of a local linear compared to a local quadratic function. Hahn et al. (2001) show that the degree of the to A.18 show further robustness for the RKD using gains.
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