R/README.md
In stharms/realEstAnalytics.r: Interfacing with Zillow's API in R
realEstAnalytics
The goal of realEstAnalytics is to provide an R function for each zillow API service, making it easy to make API calls and process the responses into R-friendly data structures.
Website
The package is hosted at https://www.realestanalyticsr.com/
Installation
You can install the released version of realEstAnalytics from Github with:
library(devtools)
devtools::install_github("xiyuansun/realEstAnalytics")
Example
Here are some basic examples of interfacing with Zillow's API in R. All calls to the API require a unique Zillow API key, which you can acquire by signing up at https://www.zillow.com/howto/api/APIOverview.htm .
First, you should always set your API key, also known as a Zillow Web Service ID (ZWSID):
library(realEstAnalytics)
#set the ZWS_ID
set_zillow_web_service_id('YOUR_API_KEY')
#retrieve the current ZWS_ID in use
zapi_key = getOption('ZillowR-zws_id')
You can get basic information on a property based on its address using GetDeepSearchResults. The return is a dataframe with the property's estimated value as well as other characteristics (i.e., bedrooms, bathrooms, etc.). To get results for more than one address at once, we recommend GetDeepSearchResults_dataframe rather than using an apply() or other mapping function.
GetDeepSearchResults('600 S. Quail Ct.', city='Newton',state='KS', zipcode=NULL,
api_key=getOption('ZillowR-zws_id'))
#> # A tibble: 1 x 26
#> address zipcode city state lat long region_name region_id type
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 600 S ~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> # ... with 17 more variables: zestimate <dbl>, zest_lastupdated <date>,
#> # zest_monthlychange <dbl>, zest_percentile <dbl>, zestimate_low <dbl>,
#> # zestimate_high <dbl>, zpid <chr>, bathrooms <dbl>, bedrooms <dbl>,
#> # finishedSqFt <dbl>, lastSoldDate <chr>, lastSoldPrice <dbl>,
#> # lotSizeSqFt <dbl>, taxAssessment <dbl>, taxAssessmentYear <dbl>,
#> # totalRooms <dbl>, yearBuilt <dbl>
Use the GetComps or GetDeepComps to get comparable properties for a given Zillow Property ID (limit 25 comparables). The return is a data frame with just the comparable addresses and their Zestimate values, with more property information available with GetDeepComps.
#retrieve the zpid from GetDeepSearchResults
zpidex <- GetDeepSearchResults('600 S. Quail Ct.', zipcode=67114,
rentzestimate=TRUE, api_key=getOption('ZillowR-zws_id'))$zpid
#GetComps for the '600 S. Quail Ct.' address
GetComps(zpidex, count=10, rentzestimate=TRUE, api_key = getOption('ZillowR-zws_id'))
#> # A tibble: 11 x 22
#> address zipcode city state lat long region_name region_id type
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 600 S ~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> 2 2137 B~ 67114 Newt~ KS 38.0 -97.4 Newton 19619 city
#> 3 2112 B~ 67114 Newt~ KS 38.0 -97.4 Newton 19619 city
#> 4 427 Vi~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> 5 410 Vi~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> 6 2109 S~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> 7 605 Au~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> 8 506 Au~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> 9 618 Au~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> 10 3000 A~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> 11 605 S ~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> # ... with 13 more variables: zestimate <dbl>, zest_lastupdated <date>,
#> # zest_monthlychange <dbl>, zest_percentile <dbl>, zestimate_low <dbl>,
#> # zestimate_high <dbl>, rentzestimate <dbl>, rent_lastupdated <date>,
#> # rent_monthlychange <dbl>, rentzestimate_low <dbl>,
#> # rentzestimate_high <dbl>, zpid <chr>, compscore <dbl>
You can get the Zestimate (Zillow's estimated home value) with GetZestimate. The return is a data frame with the Zillow-estimated value of the home, either the property value or the estimated rental value (if rentzestimate=TRUE). This function works with either a single Zillow property ID or a vector of IDs:
#GetZestimate with a vector input
GetZestimate(zpids= zpidex ,
rentzestimate=TRUE , api_key=getOption('ZillowR-zws_id'))
#> # A tibble: 1 x 20
#> address zipcode city state lat long region_name region_id type
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 600 S ~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> # ... with 11 more variables: zestimate <dbl>, zest_lastupdated <date>,
#> # zest_monthlychange <dbl>, zest_percentile <dbl>, zestimate_low <dbl>,
#> # zestimate_high <dbl>, rentzestimate <dbl>, rent_lastupdated <date>,
#> # rent_monthlychange <dbl>, rentzestimate_low <dbl>,
#> # rentzestimate_high <dbl>
There are also a few options for retrieving non-API data. Get the time series of property values aggregated by region and type with get_rental_listings and/or get_ZHVI_series. These return dataframes of time series data for the selected geographic aggregation level and property type, read from a static .csv hosted by Zillow. For options on the arguments, see the introduction vignette.
#Pull the data by state and zipcode for 4 bedrooms
cityseries <- get_ZHVI_series(bedrooms=4,geography="Zip")
#> [1] "attempting to read file:"
#> [1] "http://files.zillowstatic.com/research/public/Zip/Zip_Zhvi_4bedroom.csv"
#> Parsed with column specification:
#> cols(
#> .default = col_double(),
#> RegionName = col_character(),
#> City = col_character(),
#> State = col_character(),
#> Metro = col_character(),
#> CountyName = col_character()
#> )
#> See spec(...) for full column specifications.
head(cityseries)
#> # A tibble: 6 x 283
#> RegionID RegionName City State Metro CountyName SizeRank `1996-04`
#> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 84654 60657 Chic~ IL Chic~ Cook Coun~ 1 381400
#> 2 91982 77494 Katy TX Hous~ Harris Co~ 2 241400
#> 3 84616 60614 Chic~ IL Chic~ Cook Coun~ 3 551500
#> 4 91940 77449 Katy TX Hous~ Harris Co~ 4 109900
#> 5 93144 79936 El P~ TX El P~ El Paso C~ 5 102400
#> 6 91733 77084 Hous~ TX Hous~ Harris Co~ 6 121000
#> # ... with 275 more variables: `1996-05` <dbl>, `1996-06` <dbl>,
#> # `1996-07` <dbl>, `1996-08` <dbl>, `1996-09` <dbl>, `1996-10` <dbl>,
#> # `1996-11` <dbl>, `1996-12` <dbl>, `1997-01` <dbl>, `1997-02` <dbl>,
#> # `1997-03` <dbl>, `1997-04` <dbl>, `1997-05` <dbl>, `1997-06` <dbl>,
#> # `1997-07` <dbl>, `1997-08` <dbl>, `1997-09` <dbl>, `1997-10` <dbl>,
#> # `1997-11` <dbl>, `1997-12` <dbl>, `1998-01` <dbl>, `1998-02` <dbl>,
#> # `1998-03` <dbl>, `1998-04` <dbl>, `1998-05` <dbl>, `1998-06` <dbl>,
#> # `1998-07` <dbl>, `1998-08` <dbl>, `1998-09` <dbl>, `1998-10` <dbl>,
#> # `1998-11` <dbl>, `1998-12` <dbl>, `1999-01` <dbl>, `1999-02` <dbl>,
#> # `1999-03` <dbl>, `1999-04` <dbl>, `1999-05` <dbl>, `1999-06` <dbl>,
#> # `1999-07` <dbl>, `1999-08` <dbl>, `1999-09` <dbl>, `1999-10` <dbl>,
#> # `1999-11` <dbl>, `1999-12` <dbl>, `2000-01` <dbl>, `2000-02` <dbl>,
#> # `2000-03` <dbl>, `2000-04` <dbl>, `2000-05` <dbl>, `2000-06` <dbl>,
#> # `2000-07` <dbl>, `2000-08` <dbl>, `2000-09` <dbl>, `2000-10` <dbl>,
#> # `2000-11` <dbl>, `2000-12` <dbl>, `2001-01` <dbl>, `2001-02` <dbl>,
#> # `2001-03` <dbl>, `2001-04` <dbl>, `2001-05` <dbl>, `2001-06` <dbl>,
#> # `2001-07` <dbl>, `2001-08` <dbl>, `2001-09` <dbl>, `2001-10` <dbl>,
#> # `2001-11` <dbl>, `2001-12` <dbl>, `2002-01` <dbl>, `2002-02` <dbl>,
#> # `2002-03` <dbl>, `2002-04` <dbl>, `2002-05` <dbl>, `2002-06` <dbl>,
#> # `2002-07` <dbl>, `2002-08` <dbl>, `2002-09` <dbl>, `2002-10` <dbl>,
#> # `2002-11` <dbl>, `2002-12` <dbl>, `2003-01` <dbl>, `2003-02` <dbl>,
#> # `2003-03` <dbl>, `2003-04` <dbl>, `2003-05` <dbl>, `2003-06` <dbl>,
#> # `2003-07` <dbl>, `2003-08` <dbl>, `2003-09` <dbl>, `2003-10` <dbl>,
#> # `2003-11` <dbl>, `2003-12` <dbl>, `2004-01` <dbl>, `2004-02` <dbl>,
#> # `2004-03` <dbl>, `2004-04` <dbl>, `2004-05` <dbl>, `2004-06` <dbl>,
#> # `2004-07` <dbl>, `2004-08` <dbl>, ...
Please see the vignette (under articles) to see further documentation and examples of realEstAnalytics' capabilities.
stharms/realEstAnalytics.r documentation built on May 14, 2019, 1:57 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
realEstAnalytics
The goal of realEstAnalytics is to provide an R function for each zillow API service, making it easy to make API calls and process the responses into R-friendly data structures.
Website
The package is hosted at https://www.realestanalyticsr.com/
Installation
You can install the released version of realEstAnalytics from Github with:
library(devtools)
devtools::install_github("xiyuansun/realEstAnalytics")
Example
Here are some basic examples of interfacing with Zillow's API in R. All calls to the API require a unique Zillow API key, which you can acquire by signing up at https://www.zillow.com/howto/api/APIOverview.htm .
First, you should always set your API key, also known as a Zillow Web Service ID (ZWSID):
library(realEstAnalytics)
#set the ZWS_ID
set_zillow_web_service_id('YOUR_API_KEY')
#retrieve the current ZWS_ID in use
zapi_key = getOption('ZillowR-zws_id')
You can get basic information on a property based on its address using GetDeepSearchResults. The return is a dataframe with the property's estimated value as well as other characteristics (i.e., bedrooms, bathrooms, etc.). To get results for more than one address at once, we recommend GetDeepSearchResults_dataframe rather than using an apply() or other mapping function.
GetDeepSearchResults('600 S. Quail Ct.', city='Newton',state='KS', zipcode=NULL,
api_key=getOption('ZillowR-zws_id'))
#> # A tibble: 1 x 26
#> address zipcode city state lat long region_name region_id type
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 600 S ~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> # ... with 17 more variables: zestimate <dbl>, zest_lastupdated <date>,
#> # zest_monthlychange <dbl>, zest_percentile <dbl>, zestimate_low <dbl>,
#> # zestimate_high <dbl>, zpid <chr>, bathrooms <dbl>, bedrooms <dbl>,
#> # finishedSqFt <dbl>, lastSoldDate <chr>, lastSoldPrice <dbl>,
#> # lotSizeSqFt <dbl>, taxAssessment <dbl>, taxAssessmentYear <dbl>,
#> # totalRooms <dbl>, yearBuilt <dbl>
Use the GetComps or GetDeepComps to get comparable properties for a given Zillow Property ID (limit 25 comparables). The return is a data frame with just the comparable addresses and their Zestimate values, with more property information available with GetDeepComps.
#retrieve the zpid from GetDeepSearchResults
zpidex <- GetDeepSearchResults('600 S. Quail Ct.', zipcode=67114,
rentzestimate=TRUE, api_key=getOption('ZillowR-zws_id'))$zpid
#GetComps for the '600 S. Quail Ct.' address
GetComps(zpidex, count=10, rentzestimate=TRUE, api_key = getOption('ZillowR-zws_id'))
#> # A tibble: 11 x 22
#> address zipcode city state lat long region_name region_id type
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 600 S ~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> 2 2137 B~ 67114 Newt~ KS 38.0 -97.4 Newton 19619 city
#> 3 2112 B~ 67114 Newt~ KS 38.0 -97.4 Newton 19619 city
#> 4 427 Vi~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> 5 410 Vi~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> 6 2109 S~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> 7 605 Au~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> 8 506 Au~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> 9 618 Au~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> 10 3000 A~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> 11 605 S ~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> # ... with 13 more variables: zestimate <dbl>, zest_lastupdated <date>,
#> # zest_monthlychange <dbl>, zest_percentile <dbl>, zestimate_low <dbl>,
#> # zestimate_high <dbl>, rentzestimate <dbl>, rent_lastupdated <date>,
#> # rent_monthlychange <dbl>, rentzestimate_low <dbl>,
#> # rentzestimate_high <dbl>, zpid <chr>, compscore <dbl>
You can get the Zestimate (Zillow's estimated home value) with GetZestimate. The return is a data frame with the Zillow-estimated value of the home, either the property value or the estimated rental value (if rentzestimate=TRUE). This function works with either a single Zillow property ID or a vector of IDs:
#GetZestimate with a vector input
GetZestimate(zpids= zpidex ,
rentzestimate=TRUE , api_key=getOption('ZillowR-zws_id'))
#> # A tibble: 1 x 20
#> address zipcode city state lat long region_name region_id type
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 600 S ~ 67114 Newt~ KS 38.0 -97.3 Newton 19619 city
#> # ... with 11 more variables: zestimate <dbl>, zest_lastupdated <date>,
#> # zest_monthlychange <dbl>, zest_percentile <dbl>, zestimate_low <dbl>,
#> # zestimate_high <dbl>, rentzestimate <dbl>, rent_lastupdated <date>,
#> # rent_monthlychange <dbl>, rentzestimate_low <dbl>,
#> # rentzestimate_high <dbl>
There are also a few options for retrieving non-API data. Get the time series of property values aggregated by region and type with get_rental_listings and/or get_ZHVI_series. These return dataframes of time series data for the selected geographic aggregation level and property type, read from a static .csv hosted by Zillow. For options on the arguments, see the introduction vignette.
#Pull the data by state and zipcode for 4 bedrooms
cityseries <- get_ZHVI_series(bedrooms=4,geography="Zip")
#> [1] "attempting to read file:"
#> [1] "http://files.zillowstatic.com/research/public/Zip/Zip_Zhvi_4bedroom.csv"
#> Parsed with column specification:
#> cols(
#> .default = col_double(),
#> RegionName = col_character(),
#> City = col_character(),
#> State = col_character(),
#> Metro = col_character(),
#> CountyName = col_character()
#> )
#> See spec(...) for full column specifications.
head(cityseries)
#> # A tibble: 6 x 283
#> RegionID RegionName City State Metro CountyName SizeRank `1996-04`
#> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 84654 60657 Chic~ IL Chic~ Cook Coun~ 1 381400
#> 2 91982 77494 Katy TX Hous~ Harris Co~ 2 241400
#> 3 84616 60614 Chic~ IL Chic~ Cook Coun~ 3 551500
#> 4 91940 77449 Katy TX Hous~ Harris Co~ 4 109900
#> 5 93144 79936 El P~ TX El P~ El Paso C~ 5 102400
#> 6 91733 77084 Hous~ TX Hous~ Harris Co~ 6 121000
#> # ... with 275 more variables: `1996-05` <dbl>, `1996-06` <dbl>,
#> # `1996-07` <dbl>, `1996-08` <dbl>, `1996-09` <dbl>, `1996-10` <dbl>,
#> # `1996-11` <dbl>, `1996-12` <dbl>, `1997-01` <dbl>, `1997-02` <dbl>,
#> # `1997-03` <dbl>, `1997-04` <dbl>, `1997-05` <dbl>, `1997-06` <dbl>,
#> # `1997-07` <dbl>, `1997-08` <dbl>, `1997-09` <dbl>, `1997-10` <dbl>,
#> # `1997-11` <dbl>, `1997-12` <dbl>, `1998-01` <dbl>, `1998-02` <dbl>,
#> # `1998-03` <dbl>, `1998-04` <dbl>, `1998-05` <dbl>, `1998-06` <dbl>,
#> # `1998-07` <dbl>, `1998-08` <dbl>, `1998-09` <dbl>, `1998-10` <dbl>,
#> # `1998-11` <dbl>, `1998-12` <dbl>, `1999-01` <dbl>, `1999-02` <dbl>,
#> # `1999-03` <dbl>, `1999-04` <dbl>, `1999-05` <dbl>, `1999-06` <dbl>,
#> # `1999-07` <dbl>, `1999-08` <dbl>, `1999-09` <dbl>, `1999-10` <dbl>,
#> # `1999-11` <dbl>, `1999-12` <dbl>, `2000-01` <dbl>, `2000-02` <dbl>,
#> # `2000-03` <dbl>, `2000-04` <dbl>, `2000-05` <dbl>, `2000-06` <dbl>,
#> # `2000-07` <dbl>, `2000-08` <dbl>, `2000-09` <dbl>, `2000-10` <dbl>,
#> # `2000-11` <dbl>, `2000-12` <dbl>, `2001-01` <dbl>, `2001-02` <dbl>,
#> # `2001-03` <dbl>, `2001-04` <dbl>, `2001-05` <dbl>, `2001-06` <dbl>,
#> # `2001-07` <dbl>, `2001-08` <dbl>, `2001-09` <dbl>, `2001-10` <dbl>,
#> # `2001-11` <dbl>, `2001-12` <dbl>, `2002-01` <dbl>, `2002-02` <dbl>,
#> # `2002-03` <dbl>, `2002-04` <dbl>, `2002-05` <dbl>, `2002-06` <dbl>,
#> # `2002-07` <dbl>, `2002-08` <dbl>, `2002-09` <dbl>, `2002-10` <dbl>,
#> # `2002-11` <dbl>, `2002-12` <dbl>, `2003-01` <dbl>, `2003-02` <dbl>,
#> # `2003-03` <dbl>, `2003-04` <dbl>, `2003-05` <dbl>, `2003-06` <dbl>,
#> # `2003-07` <dbl>, `2003-08` <dbl>, `2003-09` <dbl>, `2003-10` <dbl>,
#> # `2003-11` <dbl>, `2003-12` <dbl>, `2004-01` <dbl>, `2004-02` <dbl>,
#> # `2004-03` <dbl>, `2004-04` <dbl>, `2004-05` <dbl>, `2004-06` <dbl>,
#> # `2004-07` <dbl>, `2004-08` <dbl>, ...
Please see the vignette (under articles) to see further documentation and examples of realEstAnalytics' capabilities.
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