Do sunk costs affect prices in the housing market?
Dimuthu Ratnadiwakara and Vijay Yerramilli
30 Dec 2020
rm(list=ls())
library(stargazer)
library(plyr)
library(lfe)
library(zoo)
library(scales)
library(rdrobust)
library(data.table)
library(ggplot2)
library(dplyr)
library(fst)
library(fixest)zdata[,Selling_premium:=zdata$sales_price/zdata$adj_purch_price]
zdata[,Renter_fraction:=zdata$renters/zdata$totalpopulation]
zdata[,Purchase_price:=SalesPriceAmount_prev]
zdata[,Listing_price:=listing_amount]
zdata[,Selling_price:=sales_price]
zdata[,Listing_premium:=list_premium]
zdata[,Property_tax_last_year:=prop_tax_prev_year]
zdata[,Property_tax_rate_last_year:=prop_tax_rate]
zdata_reg <- zdata[PurchaseYear<2008 & ListingYear>2013 & house_age>0 ]
zdata_reg[,Time_on_market:=as.numeric(sale_date)-as.numeric(listed_date)+1]
zdata_reg[,zip_list_month:=paste(zip,ListingMonth)]
zdata_reg[,tract_list_month:=paste(GEOID,ListingMonth)]
zdata_reg[,zip3:=floor(zip/100)]
setorder(zdata_reg,listed_date)zdata_reg[,predprice:= 0]
zd <- NULL
listmonths <- unique(zdata_reg$ListingMonth)
# pb <- txtProgressBar(min=1, max=length(listmonths), style=3)
for(i in 1:length(listmonths)) {
# setTxtProgressBar(pb, i)
temp <- CA_all[Sale_month < listmonths[i] & Sale_month> as.Date(paste0((year(listmonths[i])-1),"-",month(listmonths[i]),"-01")) & hpi_inflation>0 & sqft>0 & house_age>0 & !is.na(Purchase_price) & !is.na(hpi_inflation) & !is.na(beds) & !is.na(baths) & !is.na(sqft) & !is.na(house_age) & !is.na(yr) & !is.na(sales_price) & sales_price>0 & LotSizeSquareFeet>1000 & LotSizeSquareFeet<500000 & sales_price<2000000 & sales_price>100000 & adj_purch_price<2000000 & adj_purch_price>100000 & abs(sales_price-adj_purch_price) < 100000 ]
temp[,adjbin:=ntile(adj_purch_price,5)]
temp[,zipquin:=paste(zip,adjbin)]
adjbinmaxmin <- temp[,.(minap=min(adj_purch_price),maxap=max(adj_purch_price)),by=adjbin]
t <- zdata_reg[ListingMonth==listmonths[i]]
t[,adjbin:=ifelse(adj_purch_price < adjbinmaxmin[adjbin==1]$maxap,1,
ifelse(adj_purch_price < adjbinmaxmin[adjbin==2]$maxap,2,
ifelse(adj_purch_price < adjbinmaxmin[adjbin==3]$maxap,3,
ifelse(adj_purch_price < adjbinmaxmin[adjbin==4]$maxap,4,5))))]
t[,zipquin:=paste(zip,adjbin)]
if(nrow(temp)<30) {
t[,predprice:=adj_purch_price]
} else {
hedonicmodel <- feols(data=temp,sales_price~adj_purch_price+(beds)*factor(adjbin)+(baths)*factor(adjbin)+log(sqft)*factor(adjbin)+log(house_age)*factor(adjbin)+log(LotSizeSquareFeet)*factor(adjbin)|zipquin)
t[,predprice:=(predict(hedonicmodel,newdata = t))]
}
zd <- rbind(zd,t)
}
zdata_reg <- zd
rm(zd)# zdata_reg[,predprice:=adj_purch_price]#exp(predict(hedonicmodel,newdata = zd))]
# zdata_reg[,predprice:=(predict(hedonicmodel,newdata = zdata_reg))]#
# zdata_reg[,predprice:=exp(predict(hedonicmodel,newdata = zdata_reg))]#
zdata_reg[,nominalloss:=Purchase_price-predprice]
zdata_reg[,nominalloss:=ifelse(nominalloss<=0,1,nominalloss)]
zdata_reg[,lppp:=listing_amount/predprice]
zdata_reg[,sppp:=sales_price/predprice]
zdata_reg <- zdata_reg[abs(predprice-sales_price)<400000]
zdata_reg[,lp:=floor(listing_amount/1000)*1000]
temp <- zdata_reg[,.(predprice=mean(predprice,na.rm=T),adj_purc=mean(adj_purch_price,na.rm=T),.N),by=lp]Figure 2
# ggplot(temp[N>10 & lp<500000],aes(x=lp,y=adj_purc))+geom_point()+geom_smooth(method="lm")+theme_minimal()+labs(x="Listing price",y="Adjusted purchase price")+ scale_x_continuous(labels = scales::comma)+ scale_y_continuous(labels = scales::comma)+labs(title="Adjusted purchase price")
ggplot(temp[N>10 & lp<1000000 & lp>=300000],aes(x=lp,y=predprice))+geom_point()+theme_minimal()+labs(x="Listing price ($)",y="Predicted price ($)")+ scale_x_continuous(labels = scales::comma)+ scale_y_continuous(labels = scales::comma)#+labs(title="Predicted price")
ggplot(temp[N>10 & lp<1000000 & lp>=300000],aes(x=lp,y=adj_purc))+geom_point()+theme_minimal()+labs(x="Listing price ($)",y="Adjusted purchase price ($)")+ scale_x_continuous(labels = scales::comma)+ scale_y_continuous(labels = scales::comma)#+labs(title="Predicted price")
Table 1
stargazer(zdata_reg[,c("Purchase_price","predprice","Listing_price","lppp","sppp","Property_tax_last_year","Property_tax_rate_last_year","purchase_hpi","hpi_inflation","ownership_years","beds","baths","sqft","LotSizeSquareFeet","house_age","LTV_prev","avg_school_rating","avg_school_distance","totalpopulation","medianage","medianhouseholdincome","Renter_fraction")],summary.stat = c("mean","sd","p25","median","p75","n"),digits = 4,
covariate.labels =c("Purchased price ($)","Predicted price ($)","Listing price ($)","Listing price/Predicted price","Selling price/Predicted price","Effective tax rate","Property taxes paid ($)","HPI_{Purchase}","HPI_{List}/HPI_{Purchase}","Years of ownership","Number of bedrooms","Number of bathrooms","House area (sq. ft)","Lot area (sq. ft)","Age of the house (years)","Loan-to-value_{Purchase}","GreatSchools rating","Distance to schools (miles)","Census tract population","Census tract median age","Census tract median income","Census traction fraction of renters") ,type="text" ) ###
## ===========================================================================================================
## Statistic Mean St. Dev. Pctl(25) Median Pctl(75) N
## -----------------------------------------------------------------------------------------------------------
## Purchased price () 459,886.6000 357,534.9000 234,000 375,000 585,000 24,045
## Predicted price () 623,685.6000 466,338.1000 333,897.3000 540,944.4000 808,668.5000 24,045
## Listing price () 662,434.9000 471,191.6000 379,900.0000 554,807.5000 793,007.5000 24,045
## Listing price/Predicted price 1.4193 40.3413 0.8798 1.0321 1.2627 24,045
## Selling price/Predicted price 1.3814 39.4005 0.8576 1.0032 1.2206 24,045
## Effective tax rate 5,743.0460 4,070.7610 3,157.0 4,812 7,143.1 24,045
## Property taxes paid () 0.0102 0.0035 0.0081 0.0099 0.0117 24,045
## HPIPurchase 422,152.3000 249,687.5000 246,000 379,900 545,500 24,045
## HPIList/HPIPurchase 1.4410 0.6163 0.9917 1.2476 1.7170 24,045
## Years of ownership 12.5821 2.7748 11 12 14 24,045
## Number of bedrooms 3.4269 0.8638 3 3 4 24,045
## Number of bathrooms 2.4824 0.9912 2.0000 2.0000 3.0000 24,045
## House area (sq. ft) 1,992.6880 819.7463 1,413 1,820 2,400 24,045
## Lot area (sq. ft) 16,401.0700 90,397.8600 5,662 7,200 10,018 24,045
## Age of the house (years) 38.7952 22.7949 18 35 56 24,045
## Loan-to-valuePurchase 0.6303 0.3895 0.3994 0.7826 0.8000 24,021
## GreatSchools rating 6.8719 2.0293 5.3333 7.0000 8.6667 24,024
## Distance to schools (miles) 1.4145 1.1239 0.8000 1.1333 1.6667 24,029
## Census tract population 5,892.1790 2,949.6040 4,130.0000 5,380.0000 6,814.7500 24,040
## Census tract median age 40.1207 7.8549 34.6000 39.2000 44.5000 24,038
## Census tract median income 81,473.8400 31,331.7600 58,107.0000 76,635.0000 100,461.0000 24,037
## Census traction fraction of renters 0.3216 0.1709 0.1915 0.2928 0.4290 24,038
## -----------------------------------------------------------------------------------------------------------Table 2
# stargazer(hedonicmodelfelm,no.space = T,align = T,omit.stat=c("ser","f", "rsq"),style = "qje",omit.table.layout = "n", dep.var.labels.include = FALSE,covariate.labels = c("log(Purchase price)","log(HPI inflation)","Number of bedrooms","Number of bathrooms","House area","log(Age of the house)","log(Lot area)"), add.lines = list(c("Year", "Yes")),type="text")#Table 3
controls1 = "log(predprice)+beds+baths+sqft+avg_school_rating+avg_school_distance+walk_score+log(house_age)+log(LotSizeSquareFeet)+LTV_prev+medianage+log(medianhouseholdincome)+Renter_fraction|zip_list_month|"
controls2 = "log(predprice)+beds+baths+sqft+avg_school_rating+avg_school_distance+walk_score+log(house_age)+log(LotSizeSquareFeet)+LTV_prev+medianage+log(medianhouseholdincome)+Renter_fraction|tract_list_month|"
controls3 = "log(predprice)+beds+baths+sqft+avg_school_rating+avg_school_distance+walk_score+log(house_age)+log(LotSizeSquareFeet)+LTV_prev+medianage+log(medianhouseholdincome)+Renter_fraction+log(nominalloss)|tract_list_month|"
controls4 = "log(adj_purch_price)+beds+baths+sqft+avg_school_rating+avg_school_distance+walk_score+log(house_age)+log(LotSizeSquareFeet)+LTV_prev|zip_list_month|"
cluster = "|zip"
instruments = "ownership_years" #
endo_var = "Property_tax_rate_last_year"regs <- list()
regs[[1]] <- felm(as.formula(paste("I(prop_tax_rate*100)~ownership_years+",controls1,"0",cluster,sep="")),data=zdata_reg)
regs[[2]] <- felm(as.formula(paste("I(prop_tax_rate*100)~ownership_years+",controls2,"0","|GEOID",sep="")),data=zdata_reg)
# .printtable(regs)
stargazer(regs,no.space = T,align = T,omit.stat=c("ser","f", "rsq"),style = "qje",omit.table.layout = "n", dep.var.labels.include = F,covariate.labels = c("Ownership years","log(Predicted price)","Number of bedrooms","Number of bathrooms","House area","GreatSchools rating","Distance to schools","Distance to amenities","log(Age of the house)","log(Lot area)","Loan-to-value_{Purchase}","Census tract median age","log(Census tract median income)","Census tract fraction of renters"),type="text")###
## ====================================================
## (1) (2)
## ----------------------------------------------------
## Ownership years -0.058*** -0.056***
## (0.001) (0.001)
## log(Predicted price) -0.201*** -0.236***
## (0.012) (0.016)
## Number of bedrooms 0.020*** 0.020***
## (0.003) (0.006)
## Number of bathrooms -0.003 -0.005
## (0.002) (0.007)
## House area 0.0001*** 0.0001***
## (0.00001) (0.00001)
## GreatSchools rating 0.010*** 0.012
## (0.004) (0.010)
## Distance to schools 0.007 0.002
## (0.006) (0.015)
## Distance to amenities 0.00005 -0.0002
## (0.0002) (0.0003)
## log(Age of the house) -0.072*** -0.087***
## (0.008) (0.013)
## log(Lot area) 0.005 0.012
## (0.006) (0.010)
## Loan-to-valuePurchase 0.045*** 0.046***
## (0.008) (0.013)
## Census tract median age -0.001
## (0.001) (0.000)
## log(Census tract median income) 0.071***
## (0.021) (0.000)
## Census tract fraction of renters -0.001
## (0.037) (0.000)
## N 22,514 22,514
## Adjusted R2 0.536 0.553
## ====================================================Table 5
olsformula <- as.formula(paste("log(Listing_price)~Property_tax_rate_last_year+",controls1,"0",cluster,sep=""))
ivformula <- as.formula(paste("log(Listing_price)~",controls1,"(",endo_var,"~",instruments,")",cluster,sep=""))
ivformula_tract <-as.formula(paste("log(Listing_price)~",controls2,"(",endo_var,"~",instruments,")","|GEOID",sep=""))
ivformula_tract_loss <-as.formula(paste("log(Listing_price)~",controls3,"(",endo_var,"~",instruments,")","|GEOID",sep=""))
regs <- list()
regs[[1]] <- felm(olsformula,data=zdata_reg)
regs[[2]] <- felm(ivformula,data=zdata_reg)
regs[[3]] <- felm(ivformula_tract,data=zdata_reg)
regs[[4]] <- felm(ivformula_tract_loss,data=zdata_reg)
stargazer(regs,no.space = T,align = T,omit.stat=c("ser","f", "rsq"),style = "qje",omit.table.layout = "n", dep.var.labels.include = FALSE,
covariate.labels = c("Effective tax rate","log(Predicted price)","Number of bedrooms","Number of bathrooms","House area","GreatSchools rating","Distance to schools","Distance to amenities","log(Age of the house)","log(Lot area)","Loan-to-value_{Purchase}","Census tract median age","log(Census tract median income)","Census tract fraction of renters","log(Nominal loss)"),
column.labels=c("OLS","IV"),column.separate=c(1,3),
add.lines = list(c("zipmonth", "Yes", "Yes", "No","No"),
c("tractmonth", "Yes", "Yes", "No","No"),
c("Cond. F. Stat","",round(condfstat(regs[[2]])[[1]],2),round(condfstat(regs[[3]])[[1]],2),round(condfstat(regs[[4]])[[1]],2))),type="text")###
## ============================================================================
## OLS IV
## (1) (2) (3) (4)
## ----------------------------------------------------------------------------
## Effective tax rate 5.193***
## (0.620)
## log(Predicted price) 0.096*** 0.098*** 0.094*** 0.095***
## (0.007) (0.007) (0.009) (0.009)
## Number of bedrooms 0.002 0.002 0.004 0.006
## (0.004) (0.004) (0.006) (0.006)
## Number of bathrooms 0.007 0.007 0.011 0.011
## (0.005) (0.005) (0.008) (0.008)
## House area 0.0002*** 0.0002*** 0.0002*** 0.0002***
## (0.00001) (0.00001) (0.00002) (0.00002)
## GreatSchools rating 0.015*** 0.015*** 0.016*** 0.017***
## (0.003) (0.003) (0.006) (0.006)
## Distance to schools -0.006** -0.006** -0.002 -0.002
## (0.003) (0.003) (0.006) (0.005)
## Distance to amenities -0.0005*** -0.0005*** -0.001*** -0.001***
## (0.0001) (0.0001) (0.0002) (0.0002)
## log(Age of the house) -0.011* -0.010* -0.030*** -0.032***
## (0.006) (0.006) (0.008) (0.008)
## log(Lot area) 0.069*** 0.069*** 0.068*** 0.068***
## (0.007) (0.007) (0.009) (0.009)
## Loan-to-valuePurchase -0.008* -0.008** -0.013** -0.010*
## (0.004) (0.004) (0.006) (0.006)
## Census tract median age 0.004*** 0.004***
## (0.001) (0.001) (0.000) (0.000)
## log(Census tract median income) 0.102*** 0.101***
## (0.014) (0.014) (0.000) (0.000)
## Census tract fraction of renters 0.097*** 0.097***
## (0.023) (0.023) (0.000) (0.000)
## log(Nominal loss) 0.004***
## (0.001)
## `Property_tax_rate_last_year(fit)` 5.954*** 6.791*** 2.494**
## (0.842) (1.362) (1.260)
## zipmonth Yes Yes No No
## tractmonth Yes Yes No No
## Cond. F. Stat 262.54 113.64 109.37
## N 22,514 22,514 22,514 22,514
## Adjusted R2 0.939 0.939 0.949 0.950
## ============================================================================Table 6
zhvi <- readRDS("C:/Users/dratnadiwakara2/Documents/OneDrive - Louisiana State University/Raw Data/Zillow Research Data/Zip_Zhvi_SingleFamilyResidence.rds")
zhvi <- data.table(zhvi)
zhvi <- zhvi[state=="CA"]
zhvi <- zhvi[month %in% as.Date(c("2006-01-01","2014-01-01"))]
zhvi <- zhvi[,c("zipcode","month","zhvi")]
zhvi <- dcast(zhvi,zipcode~month,value.var = "zhvi")
names(zhvi) <- c("zipcode","val2006","val2014")
zhvi <- zhvi[complete.cases(zhvi),]
zhvi <- data.table(zhvi)
zhvi[,pricechange:=log(val2014/val2006)]
hist <- ggplot()+geom_histogram(data=zhvi,aes(x=pricechange),fill="dodgerblue4",color="dodgerblue4",alpha=0.75)+labs(x="Price Change 2006 to 2014",y="Number of Zipcodes")+ theme_minimal()+scale_y_continuous(labels = comma)
zhvi[,pricechangeqt:=ntile(pricechange,16)]
zhvi <- zhvi[,c("zipcode","pricechange","pricechangeqt")]
zhvi[,zipcode:=as.numeric(zipcode)]
zdata_reg <- merge(zdata_reg, zhvi, by.x="zip",by.y="zipcode",all.x=T)
# print(hist)regs <- list()
regs[[1]] <- felm(olsformula,data=zdata_reg[nominalloss>1])
regs[[2]] <- felm(olsformula,data=zdata_reg[nominalloss<=1])
regs[[3]] <- felm(olsformula,data=zdata_reg[pricechangeqt<=4])
regs[[4]] <- felm(olsformula,data=zdata_reg[pricechangeqt %in% 5:16])
regs[[5]] <- felm(ivformula,data=zdata_reg[pricechangeqt<=4])
regs[[6]] <- felm(ivformula,data=zdata_reg[pricechangeqt %in% 5:16])
stargazer(regs,no.space = T,align = T,omit.stat=c("ser","f", "rsq"),style = "qje",omit.table.layout = "n", dep.var.labels.include = FALSE,
covariate.labels = c("Effective tax rate","log(Predicted price)"),
column.labels=c("OLS","IV"),column.separate=c(4,2),
add.lines = list(c("Controls", "Yes", "Yes", "Yes","Yes","Yes","Yes"),c("zipmonth", "Yes", "Yes", "Yes","Yes","Yes","Yes"),c("Cond. F. Stat","","","","",round(condfstat(regs[[5]])[[1]],2),round(condfstat(regs[[6]])[[1]],2))),
keep=c("Property_tax_rate_last_year","predprice"),type="text")###
## ========================================================================================
## OLS IV
## (1) (2) (3) (4) (5) (6)
## ----------------------------------------------------------------------------------------
## Effective tax rate 5.663*** 8.539*** 1.706 6.578***
## (1.398) (0.745) (1.507) (0.717)
## log(Predicted price) 0.065*** 0.245*** 0.042*** 0.127*** 0.048*** 0.126***
## (0.008) (0.014) (0.007) (0.011) (0.009) (0.010)
## `Property_tax_rate_last_year(fit)` 5.187** 6.279***
## (2.411) (0.910)
## Controls Yes Yes Yes Yes Yes Yes
## zipmonth Yes Yes Yes Yes Yes Yes
## Cond. F. Stat 21.68 244.54
## N 5,457 17,057 3,293 19,202 3,293 19,202
## Adjusted R2 0.932 0.941 0.821 0.926 0.820 0.926
## ========================================================================================Table 7
regs <- list()
regs[[1]] <- felm(ivformula,data=zdata_reg[Listing_price<listing_q2])
regs[[2]] <- felm(ivformula,data=zdata_reg[Listing_price>listing_q2])
CA <- read_fst("C:/Users/dratnadiwakara2/Documents/OneDrive - Louisiana State University/Projects/Sunk Cost/Processed Data/all_homes_prev/CA_all_homes_prev.fst",columns = c("BuildingAreaSqFt","zip","Bedrooms"),as.data.table = T)
CA_summary <- CA[,.(
sqft_lb=quantile(BuildingAreaSqFt,0.25, na.rm=T),
sqft_hb=quantile(BuildingAreaSqFt,0.75, na.rm=T)),
by=zip]
temp <- CA[,.(
beds_lb=quantile(Bedrooms,0.25, na.rm=T),
beds_hb=floor(quantile(Bedrooms,0.75, na.rm=T)+0.99)),
by=zip]
CA_summary <- merge(CA_summary,temp,by="zip")
zdata_reg <- merge(zdata_reg,CA_summary,by="zip",all.x=T)
zdata_reg[,outlider_sqft:=ifelse(sqft>sqft_hb ,1,0)]
zdata_reg[,outlider_beds:=ifelse(beds>beds_hb,1,0)]
regs[[3]] <- felm(ivformula,data=zdata_reg[outlider_sqft==0 & outlider_beds==0])
regs[[4]] <- felm(ivformula,data=zdata_reg[outlider_sqft==1 | outlider_beds==1])
redfin <- read.csv("C:/Users/dratnadiwakara2/Documents/Research/UH computer27/sunkcost_2019/home_sales_redfin.csv")
redfin$month <- as.Date(redfin$month,origin="1900-01-01")
redfin <- data.table(redfin)
redfin <- redfin[redfin$month<="2016-01-01"]
redfin <- redfin[,.(median_home_sales=median(home_sales)),by=list(zip)]
redfin[,low_activiy:=ifelse(redfin$median_home_sales<quantile(redfin$median_home_sales,0.75),1,0)]
names(redfin) <- c("zip","median_home_sales_2","low_activity_2")
zdata_reg <- merge(zdata_reg,redfin,by="zip",all.x=TRUE)
regs[[5]] <- felm(ivformula,data=zdata_reg[low_activiy==1])
regs[[6]] <- felm(ivformula,data=zdata_reg[low_activiy==0])
temp <- zdata_reg[!duplicated(zdata_reg$GEOID)]
median_age <- median(temp$medianage,na.rm = TRUE)
regs[[7]] <- felm(ivformula,data=zdata_reg[medianage<=median_age])
regs[[8]] <- felm(ivformula,data=zdata_reg[medianage>median_age])
medincome <- median(temp$medianhouseholdincome,na.rm = TRUE)
regs[[9]] <- felm(ivformula,data=zdata_reg[medianhouseholdincome<=medincome])
regs[[10]] <- felm(ivformula,data=zdata_reg[medianhouseholdincome>medincome])
median_renterfrac <- median(temp$Renter_fraction,na.rm = TRUE)
regs[[11]] <- felm(ivformula,data=zdata_reg[Renter_fraction<=median_renterfrac])
regs[[12]] <- felm(ivformula,data=zdata_reg[Renter_fraction>median_renterfrac])
stargazer(regs,no.space = T,align = T,omit.stat=c("ser","f", "rsq"),style = "qje",omit.table.layout = "n", dep.var.labels.include = FALSE,
covariate.labels = c("Effective tax rate"),
column.labels=c("L Pr.","H Pr","L Sz","H Sz","L Act","H Act","L Ag","H Ag","L Inc","H Inc","L Ren","H Ren"),
add.lines = list(
c("Controls", rep("Yes",12)),
c("zipmonth", rep("Yes",12)),
c("Cond. F. Stat",round(c(condfstat(regs[[1]])[[1]],condfstat(regs[[2]])[[1]],condfstat(regs[[3]])[[1]],condfstat(regs[[4]])[[1]],condfstat(regs[[5]])[[1]],condfstat(regs[[6]])[[1]],condfstat(regs[[7]])[[1]],condfstat(regs[[8]])[[1]],condfstat(regs[[9]])[[1]],condfstat(regs[[10]])[[1]],condfstat(regs[[11]])[[1]],condfstat(regs[[12]])[[1]]),2))),
keep=c("Property_tax_rate_last_year"), flip=T,
type="text")###
## =============================================================================================================================
## L Pr. H Pr L Sz H Sz L Act H Act L Ag H Ag L Inc H Inc L Ren H Ren
## (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
## -----------------------------------------------------------------------------------------------------------------------------
## Effective tax rate 1.976* 8.288*** 5.028*** 9.375*** 7.098*** 5.220*** 3.594** 7.992*** 11.171*** 5.408*** 5.673*** 8.142***
## (1.075) (1.344) (1.058) (1.859) (1.352) (1.077) (1.464) (1.179) (1.809) (0.991) (1.096) (1.575)
## Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
## zipmonth Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
## Cond. F. Stat 127.39 102.84 155.93 80.89 121.51 150.58 78.99 175.84 56.71 200.44 148.44 88.44
## N 11,269 11,137 14,393 8,121 9,707 11,652 8,646 13,868 7,704 14,810 14,608 7,906
## Adjusted R2 0.956 0.951 0.937 0.939 0.931 0.936 0.937 0.933 0.905 0.934 0.943 0.931
## =============================================================================================================================Table 8 (Panel A)
regs <- list()
regs[[1]] <- felm(as.formula(paste("log(sales_price)~Property_tax_rate_last_year+",controls1,"0",cluster,sep="")),data=zdata_reg)
regs[[2]] <- felm(as.formula(paste("log(sales_price)~",controls1,"(",endo_var,"~",instruments,")",cluster,sep="")),data=zdata_reg)
regs[[3]] <- felm(as.formula(paste("log(Time_on_market)~Property_tax_rate_last_year+",controls1,"0",cluster,sep="")),data=zdata_reg)
regs[[4]] <- felm(as.formula(paste("log(Time_on_market)~",controls1,"(",endo_var,"~",instruments,")",cluster,sep="")),data=zdata_reg)
stargazer(regs,no.space = T,align = T,omit.stat=c("ser","f", "rsq"),style = "qje",omit.table.layout = "n", dep.var.labels.include = FALSE,
covariate.labels = c("Effective tax rate","log(Predicted price)"),
column.labels=c("log(Sales price)","log(Days on market)"),column.separate=c(2,2),
add.lines = list(c("Controls", "Yes", "Yes", "Yes","Yes","Yes","Yes"),c("zipmonth", "Yes", "Yes", "Yes","Yes","Yes","Yes"),c("Cond. F. Stat","",round(condfstat(regs[[2]])[[1]],2),"",round(condfstat(regs[[4]])[[1]],2))),
keep=c("Property_tax_rate_last_year","predprice"), type="text")# ##
## ========================================================================
## log(Sales price) log(Days on market)
## (1) (2) (3) (4)
## ------------------------------------------------------------------------
## Effective tax rate 5.423*** 0.193
## (0.658) (1.695)
## log(Predicted price) 0.097*** 0.099*** -0.020** -0.006
## (0.007) (0.007) (0.010) (0.012)
## `Property_tax_rate_last_year(fit)` 6.147*** 6.401**
## (0.913) (3.006)
## Controls Yes Yes Yes Yes
## zipmonth Yes Yes Yes Yes
## Cond. F. Stat 262.54 262.54
## N 22,514 22,514 22,514 22,514
## Adjusted R2 0.912 0.912 0.340 0.340
## ========================================================================temp <- zdata_reg
temp[,purch_month_no:=as.numeric(format(temp$PurchaseMonth,"%m"))]
temp[,sale_month_no:=as.numeric(format(temp$sale_date,"%m"))]
temp <- temp[!is.na(temp$predprice) & !is.na(temp$GEOID)]
temp[,zip_purch_list:=paste(temp$zip,temp$purchase_list_year)]
adj_proptax <- felm(Property_tax_rate_last_year~log(predprice)|zip_purch_list,data=temp)
adj_proptax_resid <- data.frame(adj_proptax$residuals)
names(adj_proptax_resid) <- c("adj_proptax_resid")
adj_list <- felm(log(Listing_price)~log(predprice)|zip_purch_list,data=temp)
adj_list_resid <- data.frame(adj_list$residuals)
names(adj_list_resid) <- c("adj_list_resid")
adj_sale <- felm(log(Selling_price)~log(predprice)|zip_purch_list,data=temp)
adj_sale_resid <- data.frame(adj_sale$residuals)
names(adj_sale_resid) <- c("adj_sale_resid")
adj_purch <- felm(log(Purchase_price)~0|zip_purch_list,data=temp)
adj_purch_resid <- data.frame(adj_purch$residuals)
names(adj_purch_resid) <- c("adj_purch_resid")
temp <- cbind(temp,adj_proptax_resid)
temp <- cbind(temp,adj_list_resid)
temp <- cbind(temp,adj_sale_resid)
temp <- cbind(temp,adj_purch_resid)
temp[,DocumentDate_prev:=as.Date(temp$DocumentDate_prev,origin = "1970-01-01")]
temp[,purch_week:=as.numeric(strftime(temp$DocumentDate_prev,format="%V"))-20]
temp[,postJune:=ifelse(purch_week>-2,1,0)]
temp[,zip_purch_year:=paste(zip,PurchaseYear)]Table 9
dscregs <- list()
dscregs[[1]] <- felm(log(Purchase_price)~postJune+purch_week|zip_purch_year|0|zip,data=temp)
dscregs[[2]] <- felm(beds~postJune+purch_week|zip_purch_year|0|zip,data=temp)
dscregs[[3]] <- felm(baths~postJune+purch_week|zip_purch_year|0|zip,data=temp)
dscregs[[4]] <- felm(log(sqft)~postJune+purch_week|zip_purch_year|0|zip,data=temp)
dscregs[[5]] <- felm(log(house_age)~postJune+purch_week|zip_purch_year|0|zip,data=temp)
# dscregs[[6]] <- felm(log(0.0001+LTV_prev)~postJune|zip_purch_year|0|zip,data=temp)
stargazer(dscregs,no.space = T,align = T,omit.stat=c("ser","f", "rsq"),style = "qje",omit.table.layout = "n", dep.var.labels.include = F,
covariate.labels = c("Purchased after June 01"),
add.lines = list(c("zippurchmonth", "Yes", "Yes", "Yes","Yes","Yes","Yes")),
column.labels=c("log(Purchase price)","No beds","No baths","log(House area)","log(Age of house)","log(Loan-to-value)"),type = "text")##
## ==============================================================================================
## log(Purchase price) No beds No baths log(House area) log(Age of house)
## (1) (2) (3) (4) (5)
## ----------------------------------------------------------------------------------------------
## Purchased after June 01 0.036*** -0.036* -0.028 -0.022** 0.022*
## (0.010) (0.020) (0.021) (0.010) (0.013)
## purch_week 0.001** 0.001 -0.0001 0.0002 -0.001
## (0.0003) (0.001) (0.001) (0.0004) (0.0004)
## zippurchmonth Yes Yes Yes Yes Yes
## N 24,040 24,040 24,040 24,040 24,040
## Adjusted R2 0.708 0.178 0.146 0.204 0.486
## ============================================================================================== # keep=c("Property_tax_rate_last_year","predprice"),
# type="text")#Table 10 (Panel A)
regs = list()
regs[[1]] = felm(Property_tax_rate_last_year~postJune+purch_week+log(predprice)+beds+baths+sqft+avg_school_rating+avg_school_distance+walk_score+log(house_age)+log(LotSizeSquareFeet)+LTV_prev+medianage+log(medianhouseholdincome)+Renter_fraction|zip_purch_list|0|zip,data=temp)
regs[[2]] = felm(log(Listing_price)~postJune+purch_week+log(predprice)+beds+baths+sqft+avg_school_rating+avg_school_distance+walk_score+log(house_age)+log(LotSizeSquareFeet)+LTV_prev+medianage+log(medianhouseholdincome)+Renter_fraction|zip_purch_list|0|zip,data=temp)
regs[[3]] = felm(log(Selling_price)~postJune+purch_week+log(predprice)+beds+baths+sqft+avg_school_rating+avg_school_distance+walk_score+log(house_age)+log(LotSizeSquareFeet)+LTV_prev+medianage+log(medianhouseholdincome)+Renter_fraction|zip_purch_list|0|zip,data=temp)
stargazer(regs,no.space = T,align = T,omit.stat=c("ser","f", "rsq"),style = "qje",omit.table.layout = "n", dep.var.labels.include = FALSE,
covariate.labels = c("Purchased after June 01","Week of purchase","log(Predicted price)"),
column.labels=c("Effective tax rate","log(Listing price)","log(Selling price)"),
add.lines = list(c("Controls", "Yes", "Yes", "Yes","Yes","Yes","Yes"),c("zippurchmonth", "Yes", "Yes", "Yes","Yes","Yes","Yes")),
keep=c("postJune","predprice","purch_week"),type="text")###
## ================================================================================
## Effective tax rate log(Listing price) log(Selling price)
## (1) (2) (3)
## --------------------------------------------------------------------------------
## Purchased after June 01 -0.0003*** -0.005 -0.005
## (0.0001) (0.004) (0.005)
## Week of purchase 0.00002*** 0.0002 0.0002
## (0.00000) (0.0001) (0.0001)
## log(Predicted price) -0.002*** 0.081*** 0.082***
## (0.0001) (0.006) (0.006)
## Controls Yes Yes Yes
## zippurchmonth Yes Yes Yes
## N 22,514 22,514 22,514
## Adjusted R2 0.552 0.938 0.935
## ================================================================================Table 10 (Panel B)
regs = list()
regs[[1]] = felm(Property_tax_rate_last_year~postJune+purch_week+I(purch_week^2)+log(predprice)+beds+baths+sqft+avg_school_rating+avg_school_distance+walk_score+log(house_age)+log(LotSizeSquareFeet)+LTV_prev+medianage+log(medianhouseholdincome)+Renter_fraction|zip_purch_list|0|zip,data=temp)
regs[[2]] = felm(log(Listing_price)~postJune+purch_week+I(purch_week^2)+log(predprice)+beds+baths+sqft+avg_school_rating+avg_school_distance+walk_score+log(house_age)+log(LotSizeSquareFeet)+LTV_prev+medianage+log(medianhouseholdincome)+Renter_fraction|zip_purch_list|0|zip,data=temp)
regs[[3]] = felm(log(Selling_price)~postJune+purch_week+I(purch_week^2)+log(predprice)+beds+baths+sqft+avg_school_rating+avg_school_distance+walk_score+log(house_age)+log(LotSizeSquareFeet)+LTV_prev+medianage+log(medianhouseholdincome)+Renter_fraction|zip_purch_list|0|zip,data=temp)
stargazer(regs,no.space = T,align = T,omit.stat=c("ser","f", "rsq"),style = "qje",omit.table.layout = "n", dep.var.labels.include = FALSE,
covariate.labels = c("Purchased after June 01","Week of purchase","Week of purchase^{2}","log(Predicted price)"),
column.labels=c("Effective tax rate","log(Listing price)","log(Selling price)"),
add.lines = list(c("Controls", "Yes", "Yes", "Yes","Yes","Yes","Yes"),c("zippurchmonth", "Yes", "Yes", "Yes","Yes","Yes","Yes")),
keep=c("postJune","predprice","purch_week"),type="text")#, type="text"##
## ================================================================================
## Effective tax rate log(Listing price) log(Selling price)
## (1) (2) (3)
## --------------------------------------------------------------------------------
## Purchased after June 01 -0.0003*** -0.007 -0.007
## (0.0001) (0.005) (0.006)
## Week of purchase 0.00002*** 0.0003 0.0003
## (0.00000) (0.0002) (0.0002)
## Week of purchase2 0.00000 -0.00000 -0.00000
## (0.00000) (0.00001) (0.00001)
## log(Predicted price) -0.002*** 0.081*** 0.082***
## (0.0001) (0.006) (0.006)
## Controls Yes Yes Yes
## zippurchmonth Yes Yes Yes
## N 22,514 22,514 22,514
## Adjusted R2 0.552 0.938 0.935
## ================================================================================Table 10 (Panel C)
Local linear running variable (3-months)
regs = list()
regs[[1]] = felm(Property_tax_rate_last_year~postJune+purch_week+log(predprice)+beds+baths+sqft+avg_school_rating+avg_school_distance+walk_score+log(house_age)+log(LotSizeSquareFeet)+LTV_prev+medianage+log(medianhouseholdincome)+Renter_fraction|zip_purch_list|0|zip,data=temp[abs(purch_week)<=12])
regs[[2]] = felm(log(Listing_price)~postJune+purch_week+log(predprice)+beds+baths+sqft+avg_school_rating+avg_school_distance+walk_score+log(house_age)+log(LotSizeSquareFeet)+LTV_prev+medianage+log(medianhouseholdincome)+Renter_fraction|zip_purch_list|0|zip,data=temp[abs(purch_week)<=12])
regs[[3]] = felm(log(Selling_price)~postJune+purch_week+log(predprice)+beds+baths+sqft+avg_school_rating+avg_school_distance+walk_score+log(house_age)+log(LotSizeSquareFeet)+LTV_prev+medianage+log(medianhouseholdincome)+Renter_fraction|zip_purch_list|0|zip,data=temp[abs(purch_week)<=12])
stargazer(regs,no.space = T,align = T,omit.stat=c("ser","f", "rsq"),style = "qje",omit.table.layout = "n", dep.var.labels.include = FALSE,
covariate.labels = c("Purchased after June 01","Week of purchase","log(Predicted price)"),
column.labels=c("Effective tax rate","log(Listing price)","log(Selling price)"),
add.lines = list(c("Controls", "Yes", "Yes", "Yes","Yes","Yes","Yes"),c("zippurchmonth", "Yes", "Yes", "Yes","Yes","Yes","Yes")),
keep=c("postJune","predprice","purch_week"),type="text")#,type="text"##
## ================================================================================
## Effective tax rate log(Listing price) log(Selling price)
## (1) (2) (3)
## --------------------------------------------------------------------------------
## Purchased after June 01 -0.0003*** -0.010 -0.013*
## (0.0001) (0.008) (0.008)
## Week of purchase 0.00002** 0.0002 0.001
## (0.00001) (0.001) (0.001)
## log(Predicted price) -0.002*** 0.081*** 0.086***
## (0.0002) (0.008) (0.009)
## Controls Yes Yes Yes
## zippurchmonth Yes Yes Yes
## N 11,926 11,926 11,926
## Adjusted R2 0.544 0.936 0.935
## ================================================================================