ML12340A815

Official Exhibit - NYS000446-00-BD01 - Analysis by Stephen C. Sheppard Using Tolley Mls Linear Square Root

ML12340A815
Person / Time
Site:	Indian Point
Issue date:	10/22/2012
From:	Sheppard S - No Known Affiliation
To:	Atomic Safety and Licensing Board Panel
SECY RAS
References
RAS 23665, 50-247-LR, 50-286-LR, ASLBP 07-858-03-LR-BD01
Download: ML12340A815 (4)

Text

United States Nuclear Regulatory Commission Official Hearing Exhibit NYS000446 Entergy Nuclear Operations, Inc.

In the Matter of:

(Indian Point Nuclear Generating Units 2 and 3) Submitted: October 22, 2012 ASLBP #: 07-858-03-LR-BD01 Docket #: 05000247 l 05000286 Exhibit #: NYS000446-00-BD01 Identified: 10/22/2012 Admitted: 10/22/2012 Withdrawn:

Rejected: Stricken:

Other: Originally Identified as BRD000005 User: Stephen Sheppard Statistics/Data Analysis Project: IPEC name: <unnamed>

log: /home/steve/Documents/IndianPoint/Contention 17/TolleyOriginalLInearSquareRoot.smcl log type: smcl 1 . use "/home/steve/Documents/IndianPoint/Contention 17/MLS Data STATA Format.dta" 2 .

• Tolley MLS model from report 3 . regress askprice ipdist ipdistsq med_income house_age attached rail_dist pilotpay_2011 Source SS df MS Number of obs = 296 F( 7, 288) = 26.44 Model 7028465.17 7 1004066.45 Prob > F = 0.0000 Residual 10937065.5 288 37975.922 Rsquared = 0.3912 Adj Rsquared = 0.3764 Total 17965530.7 295 60900.1041 Root MSE = 194.87 askprice Coef. Std. Err. t P>ltl [95% Conf. Interval] Dr. Tolley's ipdist 79.32316 69.82126 1.14 0.257 216.7478 58.1015 original ipdistsq 19.90054 10.78365 1.85 0.066 1.324214 41.12529 med_income 2.382077 .68255 3.49 0.001 1.038658 3.725496 model house_age 5.319204 .7153479 7.44 0.000 6.727177 3.911231 attached 263.2139 31.24132 8.43 0.000 324.7042 201.7236 rail_dist 38.50069 11.35376 3.39 0.001 60.84756 16.15383 pilotpa~2011 10.38756 15.72596 0.66 0.509 20.56482 41.33994

_cons 577.5368 121.6523 4.75 0.000 338.0965 816.9771 4 .

• Tolley model with IPEC impact modeled as linear in distance 5 . regress askprice ipdist med_income house_age attached rail_dist pilotpay_2011 Source SS df MS Number of obs = 296 F( 6, 289) = 30.03 Model 6899132.97 6 1149855.5 Prob > F = 0.0000 Residual 11066397.7 289 38292.0337 Rsquared = 0.3840 Adj Rsquared = 0.3712 Total 17965530.7 295 60900.1041 Root MSE = 195.68 askprice Coef. Std. Err. t P>ltl [95% Conf. Interval] Model with ipdist 46.8946 14.10106 3.33 0.001 19.14079 74.6484 IPEC impact med_income 2.169724 .6755748 3.21 0.001 .8400531 3.499394 house_age 5.422697 .7161083 7.57 0.000 6.832146 4.013248 proportional attached 276.5584 30.51919 9.06 0.000 336.6265 216.4904 rail_dist 34.94915 11.23594 3.11 0.002 57.0638 12.8345 to linear pilotpa~2011 19.19545 15.04643 1.28 0.203 10.41904 48.80993

_cons 409.3269 80.90215 5.06 0.000 250.0948 568.559 distance.

6 .

• Tolley model with IPEC impact modeled as proportional to square root of distance 7 . generate distsquareroot=ipdist^0.5 8 . regress askprice distsquareroot med_income house_age attached rail_dist pilotpay_2011 Source SS df MS Number of obs = 296 F( 6, 289) = 29.64 Model 6843474.45 6 1140579.08 Prob > F = 0.0000 Residual 11122056.3 289 38484.6238 Rsquared = 0.3809 Adj Rsquared = 0.3681 Total 17965530.7 295 60900.1041 Root MSE = 196.17

askprice Coef. Std. Err. t P>ltl [95% Conf. Interval]

distsquare~t 149.1657 48.24852 3.09 0.002 54.20262 244.1287 Model with med_income 2.131092 .6773751 3.15 0.002 .7978782 3.464306 IPEC impact house_age 5.439066 .7180836 7.57 0.000 6.852403 4.025729 attached 277.9586 30.69289 9.06 0.000 338.3686 217.5487 proportional rail_dist 32.94105 11.14021 2.96 0.003 54.86727 11.01482 pilotpa~2011 20.22589 15.26558 1.32 0.186 9.819931 50.27171 to square root

_cons 296.0152 105.2117 2.81 0.005 88.9368 503.0936 of distance.

9 .

• Tolley model with IPEC impact modeled as proportional to square of distance

10. regress askprice ipdistsq med_income house_age attached rail_dist pilotpay_2011 Source SS df MS Number of obs = 296 F( 6, 289) = 30.60 Model 6979449.73 6 1163241.62 Prob > F = 0.0000 Residual 10986081 289 38014.1211 Rsquared = 0.3885 Adj Rsquared = 0.3758 Total 17965530.7 295 60900.1041 Root MSE = 194.97 askprice Coef. Std. Err. t P>ltl [95% Conf. Interval]

Model with ipdistsq 7.8997 2.169942 3.64 0.000 3.628806 12.17059 med_income 2.254154 .6735366 3.35 0.001 .9284944 3.579813 IPEC impact house_age 5.385319 .7133353 7.55 0.000 6.78931 3.981328 attached 272.1092 30.25947 8.99 0.000 331.6661 212.5523 proportional rail_dist 37.46601 11.32286 3.31 0.001 59.75174 15.18029 to the square pilotpa~2011 16.62003 14.74548 1.13 0.261 12.40211 45.64217

_cons 467.6234 73.78841 6.34 0.000 322.3925 612.8542 of distance.

User: Stephen Sheppard Statistics/Data Analysis Project: IPEC name: <unnamed>

log: /home/steve/Documents/IndianPoint/Contention 17/RepeatSalesAnalysisOfTolleyQuestions.sm log type: smcl 1 .

• Here is the model that is the basis of Dr. Sheppards analysis:

2 . regress nomreturn salepre74post76 distkm if nomreturn>1 & nomreturn<1, vce(cluster id)

Linear regression Number of obs = 1511 F( 2, 506) = 9.07 Prob > F = 0.0001 Rsquared = 0.0076 Root MSE = .19957 (Std. Err. adjusted for 507 clusters in id)

Robust Dr. Sheppard's nomreturn Coef. Std. Err. t P>ltl [95% Conf. Interval]

model.

salepre74~76 .0292563 .0084169 3.48 0.001 .0457926 .01272 distkm .0180762 .0055988 3.23 0.001 .029076 .0070764

_cons .1585513 .0196089 8.09 0.000 .1200264 .1970762 3 .

• Dr. Tolley raises a question about inclusion of data where one or more sale 4 .

• involved a vacant lot. Consider the impact of excluding these observations 5 .

• from the data used for the estimates:

6 . generate salewithlot=0 7 . replace salewithlot=1 if lot==1 (325 real changes made) 8 . regress nomreturn salepre74post76 distkm if (salewithlot==0 & nomreturn>1 & nomreturn<1), vce(clu Linear regression Number of obs = 1222 F( 2, 414) = 11.33 Prob > F = 0.0000 Rsquared = 0.0085 Root MSE = .18151 (Std. Err. adjusted for 415 clusters in id)

Robust Model with nomreturn Coef. Std. Err. t P>ltl [95% Conf. Interval] any vacant salepre74~76 .0423157 .0090465 4.68 0.000 .0600985 .0245328 lot data distkm .0138921 .0058859 2.36 0.019 .025462 .0023222

_cons .1519886 .0211621 7.18 0.000 .1103901 .1935871 excluded.

9 .

• This shows that excluding these sales strengthens the results used in Dr.

10.

• Sheppards analysis. The negative impact of the treatment group relative to the

11.

• control is significantly larger. The results are estimated with greater

12.

• precision.

13.

• Dr. Tolley objects to including sales that occurred during one of the times of rapid

14.

• house price increase. Dropping these observations altogether is unwarranted.

15.

• If these time periods are different the preferred approach is to include an indicator

16.

• or dummy variable in the model to account for any excess returns.

17.

• We use indicator variables for the 1984Q2 to 1988Q1 time period and a separate

18.

• indicator for the time from 1999 through 2009.

19. regress nomreturn salepre74post76 distkm dummy_80sbubble after98 if (salewithlot==0 & nomreturn>1

> , vce(cluster id)

Linear regression Number of obs = 1222 F( 4, 414) = 11.19 Prob > F = 0.0000 Model with Rsquared = 0.0251 Root MSE = .18013 vacant lot (Std. Err. adjusted for 415 clusters in id) data excluded Robust and indicator nomreturn Coef. Std. Err. t P>ltl [95% Conf. Interval] variables for salepre74~76 .0300483 .0117221 2.56 0.011 .0530906 .007006 1984-88 and distkm .0150295 .0057691 2.61 0.010 .0263699 .003689 dummy_80sb~e .0653683 .023289 2.81 0.005 .0195888 .1111477 1999-2009.

after98 .0538824 .0133926 4.02 0.000 .0275563 .0802085

_cons .1123363 .0226813 4.95 0.000 .0677514 .1569212

20. *This shows that the result of Dr. Sheppards analysis remains essentially

21.

• unaffected by accounting for the two time periods with rapid house price appreciation.

22.

• The time periods DO show unusually high returns to owning housing, but the impact of

23.

• the IPEC treatment is not statistically different from Dr. Sheppards original result.

24. *