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]
pb <- txtProgressBar(min=1, max=nrow(zdata_reg), style=3)
for(i in 1:nrow(zdata_reg)) {
setTxtProgressBar(pb, i)
temp <- CA_all[zip==zdata_reg[i]$zip & Sale_month < zdata_reg[i]$ListingMonth & Sale_month> as.Date(paste0((year(zdata_reg[i]$ListingMonth)-1),"-",month(zdata_reg[i]$ListingMonth),"-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 & beds==zdata_reg[i]$beds ]
if(nrow(temp)<5) {
zdata_reg[i,predprice:=adj_purch_price]
} else {
zdata_reg[i,predprice:=mean(temp$sales_price,na.rm=T)]
}
}##
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|======================================================================| 100%# 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 () 421,772.7000 309,432.3000 219,000 347,000 540,000 26,531
## Predicted price () 577,871.5000 378,704.0000 343,685.0000 496,600.0000 703,589.6000 26,531
## Listing price () 628,768.1000 413,689.1000 377,044.8000 545,775.8000 763,232.5000 26,531
## Listing price/Predicted price 1.1112 0.2877 0.9438 1.0646 1.2178 26,531
## Selling price/Predicted price 1.0774 0.2573 0.9233 1.0403 1.1828 26,531
## Effective tax rate 5,355.4670 3,618.6790 3,032 4,564 6,665 26,531
## Property taxes paid () 0.0105 0.0039 0.0082 0.0100 0.0121 26,531
## HPIPurchase 419,034.1000 242,528.5000 245,800 381,100 541,550 26,531
## HPIList/HPIPurchase 1.4240 0.6064 0.9852 1.2351 1.6899 26,531
## Years of ownership 12.5556 2.7507 11 12 14 26,531
## Number of bedrooms 3.4088 0.8573 3.0000 3.0000 4.0000 26,529
## Number of bathrooms 2.4433 0.9470 2.0000 2.0000 3.0000 26,432
## House area (sq. ft) 1,949.0630 775.0677 1,403.0000 1,794.0000 2,349.0000 26,501
## Lot area (sq. ft) 16,239.7800 91,213.2800 5,616.0000 7,143.0000 9,800.0000 25,167
## Age of the house (years) 38.9664 22.6594 19 35 56 26,531
## Loan-to-valuePurchase 0.6434 0.4259 0.3705 0.7845 0.8000 26,499
## GreatSchools rating 6.8789 2.0293 5.3333 7.0000 8.6667 26,505
## Distance to schools (miles) 1.4300 1.2717 0.8000 1.1333 1.6667 26,511
## Census tract population 5,859.1530 2,908.2620 4,109.0000 5,370.0000 6,772.0000 26,521
## Census tract median age 40.0726 7.8134 34.6000 39.2000 44.4000 26,519
## Census tract median income 80,579.3200 30,776.1200 57,619.0000 75,947.0000 99,464.0000 26,518
## Census traction fraction of renters 0.3229 0.1703 0.1925 0.2938 0.4298 26,519
## -----------------------------------------------------------------------------------------------------------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 -0.068*** -0.066***
## (0.001) (0.002)
## log(Predicted price) -0.213*** -0.254***
## (0.024) (0.040)
## Number of bedrooms 0.037*** 0.040***
## (0.005) (0.010)
## Number of bathrooms -0.001 -0.005
## (0.002) (0.010)
## House area 0.00004*** 0.00005***
## (0.00001) (0.00002)
## GreatSchools rating 0.003 0.009
## (0.004) (0.011)
## Distance to schools 0.006 -0.006
## (0.005) (0.013)
## Distance to amenities 0.0002 -0.00004
## (0.0002) (0.0004)
## log(Age of the house) -0.036*** -0.046***
## (0.009) (0.016)
## log(Lot area) 0.010* 0.011
## (0.006) (0.010)
## Loan-to-valuePurchase 0.115*** 0.108***
## (0.010) (0.017)
## Census tract median age -0.001
## (0.001) (0.000)
## log(Census tract median income) 0.016
## (0.021) (0.000)
## Census tract fraction of renters -0.045
## (0.039) (0.000)
## N 23,988 23,988
## Adjusted R2 0.369 0.396
## ======================================================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 1.122***
## (0.375)
## log(Predicted price) 0.270*** 0.275*** 0.249*** 0.256***
## (0.019) (0.019) (0.026) (0.025)
## Number of bedrooms -0.018*** -0.019*** -0.014** -0.007
## (0.004) (0.004) (0.007) (0.007)
## Number of bathrooms 0.007 0.007 0.011 0.009
## (0.005) (0.005) (0.008) (0.007)
## House area 0.0002*** 0.0002*** 0.0002*** 0.0002***
## (0.00001) (0.00001) (0.00002) (0.00002)
## GreatSchools rating 0.018*** 0.018*** 0.010* 0.011*
## (0.003) (0.003) (0.006) (0.006)
## Distance to schools -0.006* -0.007** -0.006 -0.005
## (0.003) (0.003) (0.005) (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) -0.035*** -0.033*** -0.046*** -0.042***
## (0.006) (0.006) (0.009) (0.009)
## log(Lot area) 0.072*** 0.072*** 0.064*** 0.062***
## (0.007) (0.007) (0.010) (0.009)
## Loan-to-valuePurchase -0.006** -0.008*** -0.011** -0.0002
## (0.003) (0.003) (0.005) (0.005)
## Census tract median age 0.004*** 0.004***
## (0.001) (0.001) (0.000) (0.000)
## log(Census tract median income) 0.113*** 0.113***
## (0.013) (0.013) (0.000) (0.000)
## Census tract fraction of renters 0.089*** 0.091***
## (0.024) (0.024) (0.000) (0.000)
## log(Nominal loss) 0.006***
## (0.001)
## `Property_tax_rate_last_year(fit)` 3.175*** 3.983*** -2.565**
## (0.686) (1.117) (1.007)
## zipmonth Yes Yes No No
## tractmonth Yes Yes No No
## Cond. F. Stat 199.27 92.15 109.7
## N 23,988 23,988 23,988 23,988
## Adjusted R2 0.939 0.939 0.947 0.949
## ============================================================================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 -0.366 1.331*** -1.494 1.394***
## (1.630) (0.385) (1.099) (0.364)
## log(Predicted price) 0.268*** 0.298*** 0.237*** 0.286*** 0.242*** 0.291***
## (0.041) (0.022) (0.043) (0.020) (0.044) (0.020)
## `Property_tax_rate_last_year(fit)` 0.491 3.251***
## (1.915) (0.736)
## Controls Yes Yes Yes Yes Yes Yes
## zipmonth Yes Yes Yes Yes Yes Yes
## Cond. F. Stat 26.2 172.1
## N 5,783 18,205 3,756 20,212 3,756 20,212
## Adjusted R2 0.925 0.948 0.828 0.924 0.827 0.924
## ========================================================================================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 0.838 3.638*** 3.546*** 3.647** 4.085*** 2.407*** 1.518 4.158*** 6.828*** 2.108*** 2.527*** 6.181***
## (0.864) (0.960) (0.860) (1.442) (1.171) (0.857) (1.161) (0.960) (1.547) (0.753) (0.850) (1.482)
## 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 110.03 70.33 141.58 48.36 94.98 108.64 69.48 123.89 52.74 144.08 119.59 65.4
## N 12,497 11,370 15,819 8,169 10,338 12,395 9,284 14,704 8,288 15,700 15,528 8,460
## Adjusted R2 0.957 0.955 0.941 0.939 0.929 0.937 0.938 0.931 0.904 0.934 0.941 0.933
## ===========================================================================================================================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 1.441*** 0.692
## (0.397) (1.274)
## log(Predicted price) 0.273*** 0.278*** -0.008 0.007
## (0.019) (0.019) (0.043) (0.043)
## `Property_tax_rate_last_year(fit)` 3.296*** 6.339***
## (0.732) (2.377)
## Controls Yes Yes Yes Yes
## zipmonth Yes Yes Yes Yes
## Cond. F. Stat 199.27 199.27
## N 23,988 23,988 23,988 23,988
## Adjusted R2 0.913 0.913 0.340 0.339
## =========================================================================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 0.034*** -0.030 -0.021 -0.022** 0.021*
## (0.010) (0.019) (0.019) (0.010) (0.012)
## purch_week 0.001** 0.001 0.00001 0.0003 -0.001
## (0.0003) (0.001) (0.001) (0.0003) (0.0004)
## zippurchmonth Yes Yes Yes Yes Yes
## N 26,521 26,519 26,422 26,491 26,521
## Adjusted R2 0.607 0.173 0.129 0.169 0.488
## ============================================================================================== # 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 -0.0005*** -0.001 -0.001
## (0.0001) (0.004) (0.004)
## Week of purchase 0.00002*** -0.0001 -0.00003
## (0.00000) (0.0001) (0.0001)
## log(Predicted price) -0.002*** 0.263*** 0.268***
## (0.0002) (0.018) (0.019)
## Controls Yes Yes Yes
## zippurchmonth Yes Yes Yes
## N 23,988 23,988 23,988
## Adjusted R2 0.410 0.939 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*** -0.005 -0.006
## (0.0001) (0.005) (0.005)
## Week of purchase 0.00002*** 0.0002 0.0002
## (0.00000) (0.0002) (0.0002)
## Week of purchase2 0.00000* -0.00001 -0.00001
## (0.00000) (0.00001) (0.00001)
## log(Predicted price) -0.002*** 0.263*** 0.268***
## (0.0002) (0.018) (0.019)
## Controls Yes Yes Yes
## zippurchmonth Yes Yes Yes
## N 23,988 23,988 23,988
## Adjusted R2 0.411 0.939 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 -0.0004*** -0.003 -0.004
## (0.0001) (0.007) (0.007)
## Week of purchase 0.00003*** -0.00000 0.00005
## (0.00001) (0.001) (0.001)
## log(Predicted price) -0.002*** 0.254*** 0.262***
## (0.0003) (0.023) (0.023)
## Controls Yes Yes Yes
## zippurchmonth Yes Yes Yes
## N 12,626 12,626 12,626
## Adjusted R2 0.409 0.939 0.938
## ================================================================================