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)See “Merging ZTRAX Data” for sample construction code. The following sample is the subset of homes that were purchased in or before 2007.
zdata_reg <- readRDS("C:/Users/dratnadiwakara2/Documents/OneDrive - Louisiana State University/Projects/MS-FIN-21-00651/second iteration/main_sample.rds")zdata_reg[,mean_tax_rate:=mean(Property_tax_rate_last_year),by=list(zip)]
zdata_reg[,tax_variation:= (Property_tax_rate_last_year-mean_tax_rate)*100]
ggplot(data=zdata_reg[abs(tax_variation)<1])+
geom_histogram(aes(x=tax_variation,y=stat(count)/sum(stat(count))),fill="dodgerblue4",color="dodgerblue4",alpha=0.75)+
labs(x="Tax Rate - Mean Zip code Tax Rate (%)",y="Fraction of Listings")+
theme_minimal()+
scale_y_continuous(labels = comma)
This figure replicates the relationship between the property tax rate and year of purchase using the main analytic sample. The plots for states IL and NY are available in the file: [enter the file name]
yofo_tax_rate <- zdata_reg[,.(mean_tax_rate=mean(Property_tax_rate_last_year)),by=list(PurchaseYear)]
ggplot(yofo_tax_rate,aes(x=PurchaseYear,y=mean_tax_rate))+
geom_line(color="dodgerblue4")+
geom_point(fill="dodgerblue4")+
theme_minimal()+
labs(x="Year of purchase",y="Property tax rate in 2016 (%)")+
scale_x_continuous(breaks = 1996:2007) 
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 () 451,405.2000 366,721.0000 225,000 361,000 575,000 25,043
## Predicted price () 600,015.2000 431,279.9000 346,363.3000 487,528.8000 716,175.0000 25,043
## Listing price () 656,090.0000 470,464.3000 386,082.5000 550,837.5000 779,112.5000 25,043
## Listing price/Predicted price 1.1335 0.5071 0.9294 1.0551 1.2258 25,043
## Selling price/Predicted price 1.0988 0.4861 0.9062 1.0263 1.1851 25,043
## Effective tax rate 5,662.5980 4,165.6510 3,117.0000 4,710.0900 7,001.0400 25,043
## Property taxes paid () 0.0104 0.0038 0.0081 0.0099 0.0119 25,043
## HPIPurchase 421,730.6000 246,198.4000 247,200 382,400 544,100 25,043
## HPIList/HPIPurchase 1.4374 0.6143 0.9899 1.2438 1.7102 25,043
## Years of ownership 12.5721 2.7679 11 12 14 25,043
## Number of bedrooms 3.4190 0.8612 3 3 4 25,043
## Number of bathrooms 2.4723 0.9872 2 2 3 25,043
## House area (sq. ft) 1,981.8300 823.0664 1,406 1,805 2,382 25,043
## Lot area (sq. ft) 17,381.1500 113,138.5000 5,662 7,200 10,018 25,043
## Age of the house (years) 39.0934 22.8029 19 35 56 25,043
## Loan-to-valuePurchase 0.6406 0.4103 0.3956 0.7841 0.8000 25,018
## GreatSchools rating 6.8557 2.0250 5.3333 7.0000 8.6667 25,016
## Distance to schools (miles) 1.4147 1.1363 0.8000 1.1333 1.6667 25,023
## Census tract population 5,876.4770 2,924.1920 4,122.0000 5,375.0000 6,813.0000 25,039
## Census tract median age 40.0738 7.8136 34.6000 39.2000 44.4000 25,037
## Census tract median income 81,070.0100 31,010.2200 57,963.2500 76,227.5000 99,688.0000 25,036
## Census traction fraction of renters 0.3227 0.1707 0.1923 0.2937 0.4296 25,037
## -----------------------------------------------------------------------------------------------------------controls1 = "log(predprice)+beds+baths+log(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+log(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+log(sqft)+avg_school_rating+avg_school_distance+walk_score+log(house_age)+log(LotSizeSquareFeet)+LTV_prev+medianage+log(medianhouseholdincome)+Renter_fraction+log(nominalloss)|zip_list_month|"
controls4 = "log(adj_purch_price)+beds+baths+log(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)
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 -0.054*** -0.052***
## (0.001) (0.001)
## log(Predicted price) -0.724*** -0.797***
## (0.020) (0.025)
## Number of bedrooms 0.039*** 0.033***
## (0.007) (0.009)
## Number of bathrooms 0.019* 0.044***
## (0.011) (0.009)
## House area 0.289*** 0.290***
## (0.034) (0.049)
## GreatSchools rating 0.013*** 0.014
## (0.004) (0.009)
## Distance to schools 0.004 0.002
## (0.004) (0.010)
## Distance to amenities -0.0003* -0.001**
## (0.0002) (0.0003)
## log(Age of the house) -0.095*** -0.109***
## (0.009) (0.014)
## log(Lot area) 0.046*** 0.050***
## (0.007) (0.010)
## Loan-to-valuePurchase 0.039*** 0.031***
## (0.007) (0.012)
## Census tract median age 0.002***
## (0.001) (0.000)
## log(Census tract median income) 0.130***
## (0.020) (0.000)
## Census tract fraction of renters 0.104***
## (0.035) (0.000)
## N 23,977 23,977
## Adjusted R2 0.619 0.653
## ====================================================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,")",cluster,sep=""))
ivformula_listingpremium <- as.formula(paste("lppp~",controls1,"(",endo_var,"~",instruments,")",cluster,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)
regs[[5]] <- felm(ivformula_listingpremium,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","log(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","Yes","Yes"),
c("tractmonth", "No", "No", "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),
round(condfstat(regs[[5]])[[1]],2))),
type="text")##
## =======================================================================================
## OLS IV
## (1) (2) (3) (4) (5)
## ---------------------------------------------------------------------------------------
## Effective tax rate 18.804***
## (0.921)
## log(Predicted price) 0.460*** 0.410*** 0.398*** 0.402*** -0.880***
## (0.020) (0.019) (0.025) (0.019) (0.033)
## Number of bedrooms 0.028*** 0.031*** 0.025*** 0.031*** 0.082**
## (0.007) (0.007) (0.008) (0.007) (0.040)
## Number of bathrooms 0.023* 0.025* 0.047*** 0.025* 0.056
## (0.012) (0.013) (0.008) (0.013) (0.037)
## log(House area) 0.154*** 0.173*** 0.166*** 0.177*** 0.042
## (0.034) (0.036) (0.052) (0.036) (0.214)
## GreatSchools rating 0.012*** 0.013*** 0.013** 0.013*** 0.023***
## (0.003) (0.003) (0.006) (0.003) (0.007)
## Distance to schools -0.004 -0.003 0.0001 -0.003 -0.002
## (0.003) (0.003) (0.006) (0.003) (0.005)
## Distance to amenities -0.0003*** -0.0003*** -0.001*** -0.0003*** -0.001*
## (0.0001) (0.0001) (0.0002) (0.0001) (0.001)
## log(Age of the house) -0.015** -0.023*** -0.030*** -0.024*** -0.055**
## (0.007) (0.007) (0.010) (0.007) (0.027)
## log(Lot area) 0.069*** 0.072*** 0.069*** 0.073*** 0.114***
## (0.007) (0.007) (0.010) (0.007) (0.018)
## Loan-to-valuePurchase 0.001 0.002 -0.003 0.002 0.002
## (0.004) (0.004) (0.006) (0.004) (0.016)
## Census tract median age 0.003*** 0.003*** 0.003*** 0.004***
## (0.0005) (0.0005) (0.000) (0.0005) (0.001)
## log(Census tract median income) 0.082*** 0.091*** 0.092*** 0.162***
## (0.012) (0.012) (0.000) (0.012) (0.040)
## Census tract fraction of renters 0.069*** 0.075*** 0.076*** 0.138***
## (0.021) (0.021) (0.000) (0.021) (0.035)
## log(Nominal loss) 0.001
## (0.0004)
## `Property_tax_rate_last_year(fit)` 12.718*** 13.401*** 11.797*** 12.388***
## (0.993) (1.564) (1.035) (1.387)
## zipmonth Yes Yes No Yes Yes
## tractmonth No No Yes No No
## Cond. F. Stat 217.95 107 185.04 217.95
## N 23,977 23,977 23,977 23,977 23,977
## Adjusted R2 0.941 0.940 0.948 0.940 0.395
## =======================================================================================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<=8])
regs[[4]] <- felm(olsformula,data=zdata_reg[pricechangeqt %in% 8:16])
regs[[5]] <- felm(ivformula,data=zdata_reg[pricechangeqt<=8])
regs[[6]] <- felm(ivformula,data=zdata_reg[pricechangeqt %in% 8:16])
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 22.437*** 16.114*** 20.015*** 18.271***
## (2.444) (1.040) (1.468) (0.990)
## log(Predicted price) 0.660*** 0.385*** 0.536*** 0.422*** 0.506*** 0.368***
## (0.049) (0.022) (0.034) (0.020) (0.036) (0.019)
## `Property_tax_rate_last_year(fit)` 17.219*** 11.016***
## (1.974) (1.047)
## Controls Yes Yes Yes Yes Yes Yes
## zipmonth Yes Yes Yes Yes Yes Yes
## Cond. F. Stat 41.7 224.04
## N 5,041 18,936 9,459 16,109 9,459 16,109
## Adjusted R2 0.931 0.951 0.896 0.918 0.895 0.916
## ==============================================================================================regs <- list()
regs[[1]] <- felm(ivformula,data=zdata_reg[Listing_price<listing_q3])
regs[[2]] <- felm(ivformula,data=zdata_reg[Listing_price>=listing_q3])
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])
popbyzip<- fread("https://raw.githubusercontent.com/fissehab/US_Population_Data/master/pop-by-zip-code.csv")
popbyzip <- popbyzip[,c("zip_code","y-2016")]
names(popbyzip) <- c("zip","population")
redfin <- read.csv("C:/Users/dratnadiwakara2/Documents/Research/UH computer27/sunkcost_2019/home_sales_redfin.csv")
redfin <- merge(redfin,popbyzip,by="zip")
redfin$month <- as.Date(redfin$month,origin="1900-01-01")
redfin <- data.table(redfin)
redfin[,home_sales:=home_sales/population]
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$median_home_sales,0.5),1,0)]
names(redfin) <- c("zip","median_home_sales_2","low_activity_2")
zdata_reg <- merge(zdata_reg,redfin,by="zip",all.x=TRUE)
regs[[5]] <- felm(ivformula,data=zdata_reg[low_activity_2==1])
regs[[6]] <- felm(ivformula,data=zdata_reg[low_activity_2==0])
temp <- zdata_reg[!duplicated(zdata_reg$GEOID)]
median_age <- median(temp$medianage,na.rm = TRUE)
zdata_reg[,agelow:=ifelse(medianage<=median_age,1,0)]
regs[[7]] <- felm(ivformula,data=zdata_reg[medianage<=median_age])
regs[[8]] <- felm(ivformula,data=zdata_reg[medianage>median_age])
medincome <- median(temp$medianhouseholdincome,na.rm = TRUE)
zdata_reg[,inclow:=ifelse(medianhouseholdincome<=medincome,1,0)]
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)
zdata_reg[,rentlow:=ifelse(Renter_fraction<=median_renterfrac,1,0)]
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 7.922*** 20.844*** 9.299*** 19.159*** 10.406*** 12.483*** 7.981*** 14.617*** 16.119*** 11.314*** 12.464*** 12.837***
## (0.959) (3.009) (1.112) (2.213) (1.245) (1.369) (1.589) (1.292) (1.977) (1.140) (1.339) (1.614)
## 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 184.17 24.4 148.39 75.78 127.23 112.32 59.17 146.68 44.11 185.36 129.84 67.56
## N 18,340 5,637 15,544 8,433 10,794 11,913 9,354 14,623 8,442 15,535 15,450 8,527
## Adjusted R2 0.950 0.955 0.936 0.945 0.926 0.946 0.933 0.936 0.908 0.934 0.941 0.939
## =======================================================================================================================================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 18.423*** 3.445*
## (0.899) (1.784)
## log(Predicted price) 0.447*** 0.399*** 0.046* 0.085**
## (0.019) (0.019) (0.026) (0.034)
## `Property_tax_rate_last_year(fit)` 12.555*** 8.222***
## (1.045) (3.162)
## Controls Yes Yes Yes Yes
## zipmonth Yes Yes Yes Yes
## Cond. F. Stat 217.95 217.95
## N 23,977 23,977 23,977 23,977
## Adjusted R2 0.916 0.915 0.336 0.336
## ==========================================================================See “Merging ZTRAX Data” for sample construction code.
rddsample <- readRDS("C:/Users/dratnadiwakara2/Documents/OneDrive - Louisiana State University/Projects/MS-FIN-21-00651/second iteration/rdd_sample.rds")regs = list()
regs[[1]] = felm(I(Property_tax_rate_last_year*100)~postJune+purch_week+I(purch_week^2)+I(purch_week^3)+log(predprice)+beds+baths+log(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=rddsample)
regs[[2]] = felm(log(Listing_price)~postJune+purch_week+I(purch_week^2)+I(purch_week^3)+log(predprice)+beds+baths+log(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=rddsample)
regs[[3]] = felm(log(Selling_price)~postJune+purch_week+I(purch_week^2)+I(purch_week^3)+log(predprice)+beds+baths+log(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=rddsample)
regs[[4]] = felm(log(Purchase_price)~postJune+purch_week+I(purch_week^2)+I(purch_week^3)+log(predprice)+beds+baths+log(sqft)+avg_school_rating+avg_school_distance+walk_score+medianage+log(medianhouseholdincome)+Renter_fraction|zip_purch_list|0|zip,data=rddsample)
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}"),
column.labels=c("Effective tax rate","log(Listing price)","log(Selling price)","log(Purchased price)"),
add.lines = list(
c("Controls", "Yes", "Yes", "Yes","Yes","Yes","Yes"),
c("zippurchmonth", "Yes", "Yes", "Yes","Yes","Yes","Yes")),
keep=c("postJune","purch_week"),type="text")##
## =====================================================================================================
## Effective tax rate log(Listing price) log(Selling price) log(Purchased price)
## (1) (2) (3) (4)
## -----------------------------------------------------------------------------------------------------
## Purchased after June 01 -0.016*** -0.009** -0.014* 0.001
## (0.005) (0.004) (0.008) (0.001)
## Week of purchase 0.001* -0.0001 0.0002 0.001***
## (0.0003) (0.0002) (0.0004) (0.0001)
## Week of purchase2 0.00001 0.00002* 0.00001 0.00001***
## (0.00001) (0.00001) (0.00002) (0.00000)
## I(purch_week3) -0.00000 -0.00000 -0.00000 -0.00000***
## (0.00000) (0.00000) (0.00000) (0.00000)
## Controls Yes Yes Yes Yes
## zippurchmonth Yes Yes Yes Yes
## N 52,489 52,489 52,489 52,522
## Adjusted R2 0.597 0.940 0.804 0.992
## =====================================================================================================