| Type: | Package |
| Title: | House Price Indexes |
| Version: | 0.3.2 |
| Maintainer: | Andy Krause <andyxkrause@gmail.com> |
| Description: | Compute house price indexes and series using a variety of different methods and models common through the real estate literature. Evaluate index 'goodness' based on accuracy, volatility and revision statistics. Background on basic model construction for repeat sales models can be found at: Case and Quigley (1991) https://ideas.repec.org/a/tpr/restat/v73y1991i1p50-58.html and for hedonic pricing models at: Bourassa et al (2006) <doi:10.1016/j.jhe.2006.03.001>. The package author's working paper on the random forest approach to house price indexes can be found at: http://www.github.com/andykrause/hpi_research. |
| Depends: | R (≥ 3.5.0) |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | dplyr, magrittr, lubridate, robustbase, ggplot2, imputeTS (≥ 3.0), purrr, forecast, gridExtra, MASS, rlang, plyr, zoo, ranger, pdp |
| URL: | https://www.github.com/andykrause/hpiR |
| RoxygenNote: | 6.1.1 |
| Suggests: | markdown, testthat, covr, knitr |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2020-04-01 15:24:12 UTC; andyk |
| Author: | Andy Krause [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2020-04-01 16:00:02 UTC |
hpiR: A package for house price indexes
Description
House Price Indexes in R: A set of tools to create house price indexes and analyze their various performance metrics.
Create the row IDs for forecast accuracy
Description
Generate a vector of row IDs for use in forecast accuracy tests
Usage
buildForecastIDs(time_cut, hpi_df, forecast_length = 1, train = TRUE)
Arguments
time_cut |
Period after which to cut off data |
hpi_df |
Data to be converted to training or scoring |
forecast_length |
default = 1; Length of forecasting to do |
train |
Default=TRUE; Create training data? FALSE = Scoring data |
Value
vector of row_ids indicating inclusion in the forecasting data as either the training set (train = TRUE) or the scoring set (train = FALSE)
Further Details
This function is rarely (if ever) used directly. Most often called by 'calcForecastError()'
It is a generic method that dispatches on the 'hpi_df' object.
Examples
# Load example sales
data(ex_sales)
# Create RT data
rt_data <- rtCreateTrans(trans_df = ex_sales,
prop_id = 'pinx',
trans_id = 'sale_id',
price = 'sale_price',
periodicity = 'monthly',
date = 'sale_date')
# Create ids
fc_ids <- buildForecastIDs(time_cut = 27,
hpi_df = rt_data,
forecast_length = 2,
train = TRUE)
Create the row IDs for forecast accuracy (hed approach)
Description
Generate a vector of row IDs for use in forecast accuracy tests (hed approach)
Usage
## S3 method for class 'heddata'
buildForecastIDs(time_cut, hpi_df, forecast_length = 1,
train = TRUE)
Arguments
time_cut |
Period after which to cut off data |
hpi_df |
Data to be converted to training or scoring |
forecast_length |
default = 1; Length of forecasting to do |
train |
Default=TRUE; Create training data? FALSE = Scoring data |
Create the row IDs for forecast accuracy (rt approach)
Description
Generate a vector of row IDs for use in forecast accuracy tests (rt approach)
Usage
## S3 method for class 'rtdata'
buildForecastIDs(time_cut, hpi_df, forecast_length = 1,
train = TRUE)
Arguments
time_cut |
Period after which to cut off data |
hpi_df |
Data to be converted to training or scoring |
forecast_length |
default = 1; Length of forecasting to do |
train |
Default=TRUE; Create training data? FALSE = Scoring data |
Calculate the accuracy of an index
Description
Estimate index accuracy using one of a variety of approaches
Usage
calcAccuracy(hpi_obj, test_method = "insample", test_type = "rt",
pred_df = NULL, smooth = FALSE, in_place = FALSE,
in_place_name = "accuracy", ...)
Arguments
hpi_obj |
Object of class 'hpi' |
test_method |
default = 'insample'; Also 'kfold' |
test_type |
default = 'rt'; Type of data to use for test. See details. |
pred_df |
default = NULL; Extra data if the test_type doesn't match data in hpi_obj |
smooth |
default = FALSE; calculated on the smoothed index(es) |
in_place |
default = FALSE; Should the result be returned into an existing 'hpi' object |
in_place_name |
default = 'accuracy'; Name for returning in place |
... |
Additional Arguments |
Value
object of class 'hpiaccuracy' inheriting from class 'data.frame' containing the following fields:
- prop_id
Property Identification number
- price
Transaction Price
- pred_price
Predicted price
- error
(Prediction - Actual) / Actual
- log_error
log(prediction) - log(actual)
- pred_period
Period of the prediction
Further Details
'rt' test type tests the ability of the index to correctly predict the second value in a repeat transaction pair FUTURE: 'hed' test type tests the ability of the index to improve an OLS model that doesn't account for time. (This approach is not ready yet).
Examples
# Load Data
data(ex_sales)
# Create Index
rt_index <- rtIndex(trans_df = ex_sales,
periodicity = 'monthly',
min_date = '2010-06-01',
max_date = '2015-11-30',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'robust',
log_dep = TRUE,
trim_model = TRUE,
max_period = 48,
smooth = FALSE)
# Calculate insample accuracy
hpi_accr <- calcAccuracy(hpi_obj = rt_index,
test_type = 'rt',
test_method = 'insample')
Calculate the forecast accuracy of series of indexes
Description
Estimate the index accuracy with forecasting for a (progressive) series of indexes
Usage
calcForecastError(is_obj, pred_df, return_forecasts = FALSE,
forecast_length = 1, ...)
Arguments
is_obj |
Object of class 'hpiseries' |
pred_df |
Set of sales to be used for predictive quality of index |
return_forecasts |
default = FALSE; return the forecasted indexes |
forecast_length |
default = 1; Length of period(s) in time to forecast |
... |
Additional Arguments |
Value
object of class 'hpiaccuracy' inheriting from class 'data.frame' containing the following fields:
- prop_id
Property Identification number
- price
Transaction Price
- pred_price
Predicted price
- error
(Prediction - Actual) / Actual
- log_error
log(prediction) - log(actual)
- pred_period
Period of the prediction
- series
Series position from which the prediction was generated
Further Details
If you set 'return_forecasts' = TRUE, the forecasted indexes for each period will be returned in the 'forecasts' attribute of the 'hpiaccuracy' object. (attr(accr_obj, 'forecasts')
For now, the 'pred_df' object must be a set of repeat transactions with the class 'rt', inheriting from 'hpidata'
Examples
# Load example sales
data(ex_sales)
# Create Index
hed_index <- hedIndex(trans_df = ex_sales,
periodicity = 'monthly',
max_date = '2011-12-31',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'robust',
log_dep = TRUE,
trim_model = TRUE,
max_period = 24,
dep_var = 'price',
ind_var = c('tot_sf', 'beds', 'baths'),
smooth = FALSE)
# Create Series (Suppressing messages do to small sample size of this example)
suppressMessages(
hpi_series <- createSeries(hpi_obj = hed_index,
train_period = 12))
# Create Prediction data
rt_data <- rtCreateTrans(trans_df = ex_sales,
prop_id = 'pinx',
max_date = '2011-12-31',
trans_id = 'sale_id',
price = 'sale_price',
periodicity = 'monthly',
date = 'sale_date',
min_period_dist = 12)
# Calculate forecast accuracty
fc_accr <- calcForecastError(is_obj = hpi_series,
pred_df = rt_data)
Calculate index errors in sample
Description
Estimate the predictive error of an index via an in-sample approach.
Usage
calcInSampleError(pred_df, index, ...)
Arguments
pred_df |
Set of sales against which to test predictions |
index |
Index (of class 'ts') to be tested for accuracy |
... |
Additional Arguments |
Value
object of class 'hpiaccuracy' inheriting from class 'data.frame' containing the following fields:
- pair_id
Uniq Pair ID number
- price
Transaction Price
- pred_price
Predicted price
- error
(Prediction - Actual) / Actual
- log_error
log(prediction) - log(actual)
- pred_period
Period of the prediction
Further Details
In addition to being a stand-alone function, it is also used by 'calcForecastError' and 'calcKFoldError“
Examples
# Load example data
data(ex_sales)
# Create index with raw transaction data
rt_index <- rtIndex(trans_df = ex_sales,
periodicity = 'monthly',
min_date = '2010-06-01',
max_date = '2015-11-30',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'robust',
log_dep = TRUE,
trim_model = TRUE,
max_period = 48,
smooth = FALSE)
# Calculate accuracy
in_accr <- calcInSampleError(pred_df = rt_index$data,
index = rt_index$index$value)
Calculate index errors in sample (hed approach)
Description
Estimate the predictive error of an index via an in-sample approach (hed approach)
Usage
## S3 method for class 'heddata'
calcInSampleError(pred_df, index, ...)
Arguments
pred_df |
Set of sales against which to test predictions |
index |
Index (of class 'ts') to be tested for accuracy |
... |
Additional Arguments |
Calculate index errors in sample (rt approach)
Description
Estimate the predictive error of an index via an in-sample approach (rt approach)
Usage
## S3 method for class 'rtdata'
calcInSampleError(pred_df, index, ...)
Arguments
pred_df |
Set of sales against which to test predictions |
index |
Index (of class 'ts') to be tested for accuracy |
... |
Additional Arguments |
Calculate index error with FKold (out of sample)
Description
Use a KFold (out of sample) approach to estimate index accuracy
Usage
calcKFoldError(hpi_obj, pred_df, k = 10, seed = 1, smooth = FALSE,
...)
Arguments
hpi_obj |
HPI object of class 'hpi' |
pred_df |
Data.frame of sales to be used for assessing predictive quality of index |
k |
default=10; Number of folds to apply to holdout process |
seed |
default=1; Random seed generator to control the folding process |
smooth |
default = FALSE; Calculate on the smoothed index |
... |
Additional Arguments |
Value
object of class 'hpiaccuracy' inheriting from class 'data.frame' containing the following fields:
- pair_id
Unique Pair ID
- price
Transaction Price
- pred_price
Predicted price
- error
(Prediction - Actual) / Actual
- log_error
log(prediction) - log(actual)
- pred_period
Period of the prediction
Examples
# Load data
data(ex_sales)
# Create index with raw transaction data
rt_index <- rtIndex(trans_df = ex_sales,
periodicity = 'monthly',
min_date = '2010-06-01',
max_date = '2015-11-30',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'robust',
log_dep = TRUE,
trim_model = TRUE,
max_period = 48,
smooth = FALSE)
# Create prediction data
rt_data <- rtCreateTrans(trans_df = ex_sales,
prop_id = 'pinx',
trans_id = 'sale_id',
price = 'sale_price',
periodicity = 'monthly',
date = 'sale_date')
# Calc Accuracy
kf_accr <- calcKFoldError(hpi_obj = rt_index,
pred_df = rt_data,
k = 10,
seed = 123,
smooth = FALSE)
Calculate revision values of an index
Description
Create estimates of the revision statistics for a house price index
Usage
calcRevision(series_obj, in_place = FALSE, in_place_name = "rev",
smooth = FALSE, ...)
Arguments
series_obj |
A list of progressively longer indexes (a 'serieshpi“ object from 'createSeries()“) |
in_place |
default = FALSE; Calculating in place (adding to hpi) |
in_place_name |
default = 'rev'; Name of revision object in_place |
smooth |
default = FALSE; Use smoothed indexes |
... |
Additional Arguments |
Value
list of length 3 containing:
- period
Data.frame containing the period number, mean and median for that period
- mean
Mean revision for all periods
- median
Median revision for all periods
Further Details
The revision object can be generate "in place" inside of the 'serieshpi' object by setting 'in_place' equal to TRUE.
Examples
# Load example sales
data(ex_sales)
# Create Index
rt_index <- rtIndex(trans_df = ex_sales,
periodicity = 'monthly',
min_date = '2010-06-01',
max_date = '2015-11-30',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'robust',
log_dep = TRUE,
trim_model = TRUE,
max_period = 48,
smooth = FALSE)
# Create Series (Suppressing messages do to small sample size of this example)
suppressMessages(
hpi_series <- createSeries(hpi_obj = rt_index,
train_period = 12))
# Calculate revision
series_rev <- calcRevision(series_obj = hpi_series)
Calculate the accuracy of a series of indexes
Description
Estimate the index accuracy for a (progressive) series of indexes
Usage
calcSeriesAccuracy(series_obj, test_method = "insample",
test_type = "rt", pred_df = NULL, smooth = FALSE,
summarize = FALSE, in_place = FALSE, in_place_name = "accuracy",
...)
Arguments
series_obj |
Serieshpi object to be analyzed |
test_method |
default = 'insample'; Also 'kfold' or 'forecast' |
test_type |
default = 'rt'; Type of data to use for test. See details. |
pred_df |
default = NULL; Extra data if the test_type doesn't match data in hpi_obj |
smooth |
default = FALSE; Analyze the smoothed indexes |
summarize |
default = FALSE; When multiple accuracy measurements for single observation take the mean of them all. |
in_place |
default = FALSE; Should the result be returned into an existing 'hpi' object |
in_place_name |
default = 'accuracy'; Name for returning in place |
... |
Additional Arguments |
Value
‘seriesaccuracy' object (unless calculated ’in_place')
Further Details
Unless using 'test_method = "forecast"“ with a "forecast_length" of 1, the results will have more than one accuracy estimate per observations. Setting 'summarize = TRUE' will take the mean accuracy for each observation across all indexes.
Examples
# Load data
data(ex_sales)
# Create index
rt_index <- rtIndex(trans_df = ex_sales,
periodicity = 'monthly',
min_date = '2010-06-01',
max_date = '2015-11-30',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'robust',
log_dep = TRUE,
trim_model = TRUE,
max_period = 48,
smooth = FALSE)
# Create Series (Suppressing messages do to small sample size of this example)
suppressMessages(
hpi_series <- createSeries(hpi_obj = rt_index,
train_period = 12))
# Calculate insample accuracy
hpi_series_accr <- calcSeriesAccuracy(series_obj = hpi_series,
test_type = 'rt',
test_method = 'insample')
Calculate volatility of a series of indexes
Description
Calculates volatility over a (progressive) series of indexes
Usage
calcSeriesVolatility(series_obj, window = 3, smooth = FALSE,
in_place_name = "volatility", ...)
Arguments
series_obj |
Series object to be calculated |
window |
default = 3; Rolling periods over which to calculate the volatility |
smooth |
default = FALSE; Also calculate volatilities for smoothed indexes |
in_place_name |
name if saving in place |
... |
Additional Arguments |
Value
'serieshpi' object
Further Details
Leaving order blank default to a moving average with order 3.
Examples
# Load example sales
data(ex_sales)
# Create Index
rt_index <- rtIndex(trans_df = ex_sales,
periodicity = 'monthly',
min_date = '2010-06-01',
max_date = '2015-11-30',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'robust',
log_dep = TRUE,
trim_model = TRUE,
max_period = 48,
smooth = FALSE)
# Create Series (Suppressing messages do to small sample size of this example)
suppressMessages(
hpi_series <- createSeries(hpi_obj = rt_index,
train_period = 12))
# Calculate series volatility
series_vol <- calcSeriesVolatility(series_obj = hpi_series,
window= 3)
Calculate index volatility
Description
Create estimate of index volatility given a window
Usage
calcVolatility(index, window = 3, in_place = FALSE,
in_place_name = "volatility", smooth = FALSE, ...)
Arguments
index |
An object of class 'hpiindex' |
window |
default = 3; Rolling periods over which to calculate the volatility |
in_place |
default = FALSE; Adds volatility metric to the 'hpiindex' object (may be within an 'hpi' object) |
in_place_name |
default = 'vol'; Name of volatility object in 'hpiindex' object |
smooth |
default = FALSE; Calculate on the smoothed index? |
... |
Additional arguments |
Value
an ‘indexvolatility' (S3) object, the ’index' slot of which is a 'ts' object
- roll
volatility at each rolling point
- mean
overall mean volatility
- median
overall median volatility
Further Details
You may also provide an 'hpi' object to this function. If you do, it will extract the 'hpiindex' object from the 'index' slot in the 'hpi' class object.
Examples
# Load Data
data(ex_sales)
# Create index with raw transaction data
rt_index <- rtIndex(trans_df = ex_sales,
periodicity = 'monthly',
min_date = '2010-06-01',
max_date = '2015-11-30',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'robust',
log_dep = TRUE,
trim_model = TRUE,
max_period = 48,
smooth = FALSE)
# Calculate Volatility
index_vol <- calcVolatility(index = rt_index,
window = 3)
Validate the date argument
Description
Internal function to validate (or convert) the provided date field
Usage
checkDate(x_date, name)
Arguments
x_date |
Date string or vector |
name |
Name of argument to return in error/warning message |
Value
Adjusted date field
Examples
# Load Data
data(ex_sales)
# Check date
date_checked <- checkDate(x_date = ex_sales$sale_date,
name = 'sale date')
Create data for KFold error test
Description
Generic method for creating KFold testing data
Usage
createKFoldData(score_ids, full_data, pred_df)
Arguments
score_ids |
Vector of row ids to be included in scoring data |
full_data |
Complete dataset (class 'hpidata“) of this model type (rt or hed) |
pred_df |
Data to be used for prediction |
Value
list of length 2 containing:
- train
Training data.frame
- score
Scoring data.frame
Further Details
Called from 'calcKFoldError()“
Examples
# Load Data
data(ex_sales)
# Create RT Data
rt_data <- rtCreateTrans(trans_df = ex_sales,
prop_id = 'pinx',
trans_id = 'sale_id',
price = 'sale_price',
periodicity = 'monthly',
date = 'sale_date')
# Create folds
k_folds <- split(x = 1:nrow(rt_data),
f = sample(1:10, nrow(rt_data), replace = TRUE))
# Create data from folds
kfold_data <- createKFoldData(score_ids = k_folds[[1]],
full_data = rt_data,
pred_df = rt_data)
Create data for KFold error test (rt approach)
Description
'rtdata' method for creating KFold testing data
Usage
## S3 method for class 'rtdata'
createKFoldData(score_ids, full_data, pred_df)
Arguments
score_ids |
Vector of row ids to be included in scoring data |
full_data |
Complete dataset (class 'hpidata“) of this model type (rt or hed) |
pred_df |
Data to be used for prediction |
Create a series of indexes
Description
Generate a series of progressive indexes
Usage
createSeries(hpi_obj, train_period = 12, max_period = NULL, ...)
Arguments
hpi_obj |
Object of class 'hpi' |
train_period |
default = 12; Number of periods to use as purely training before creating indexes |
max_period |
default = NULL; Maximum number of periods to create the index up to |
... |
Additional Arguments |
Value
An 'serieshpi' object – a list of 'hpi' objects.
Further Details
'train_period' Represents the shortest index that you will create. For certain approaches, such as a repeat transaction model, indexes shorter than 10 will likely be highly unstable.
If 'max_period“ is left NULL, then it will forecast up to the end of the data.
Examples
# Load example sales
data(ex_sales)
# Create Index
rt_index <- rtIndex(trans_df = ex_sales,
periodicity = 'monthly',
min_date = '2010-06-01',
max_date = '2015-11-30',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'robust',
log_dep = TRUE,
trim_model = TRUE,
max_period = 48,
smooth = FALSE)
# Create Series (Suppressing messages do to small sample size of this example)
suppressMessages(
hpi_series <- createSeries(hpi_obj = rt_index,
train_period = 12))
Convert dates to a relative period
Description
Create a relative period variable from a date variable
Usage
dateToPeriod(trans_df, date, periodicity = NULL, min_date = NULL,
max_date = NULL, adj_type = "move", ...)
Arguments
trans_df |
data.frame of raw transactions |
date |
name of field containing the date of the sale in Date or POSIXt format |
periodicity |
type of periodicity to use ('yearly', 'quarterly', 'monthly' or 'weekly) |
min_date |
default = NULL; optional minimum date to use |
max_date |
default = NULL; optional maximum date to use |
adj_type |
default = 'move'; how to handle min and max dates within the range of transactions. 'move' min and/or max date or 'clip' the data |
... |
Additional arguments |
Value
original data frame ('trans_df' object) with two new fields: trans_period: integer value counting from the minimum transaction date in the periodicity selected. Base value is 1. Primarily for modeling trans_date: properly formatted transaction date
Further Details
"trans_period" counts from the minimum transaction date provided. As such the period counts are relative, not absolute
Additionally, this function modifies the data.frame that it is given and return that same data.frame that it is given and returns that data.frame with the new fields attached.
Examples
# Load data
data(ex_sales)
# Convert to period df
hpi_data <- dateToPeriod(trans_df = ex_sales,
date = 'sale_date',
periodicity = 'monthly')
Subset of Seattle Home Sales
Description
Seattle home sales from areas 13, 14,an 15 (central Seattle) 2010 to 2016. Includes only detached single family residences and townhomes. Data gathered from the King County Assessor's FTP site. A number of initial data munging tasks were necessary to bring the data into this format.
Usage
data(ex_sales)
Format
A "data.frame" with 5,348 rows and 16 variables
- pinx
The unique property identifying code. Original value is preceded by two '..'s to prevent the dropping of leading zeros
- sale_id
The unique transaction identifying code.
- sale_price
Price of the home
- sale_date
Date of sale
- use_type
Property use type
- area
Assessment area or zone
- lot_sf
Size of lot in square feet
- wfnt
Is property waterfront?
- bldg_grade
Quality of the building construction (higher is better)
- tot_sf
Size of home in square feet
- beds
Number of bedrooms
- baths
Number of bathrooms
- age
Age of home
- eff_age
Age of home, considering major remodels
- longitude
Longitude
- latitude
Latitude
Source
King County Assessor: http://info.kingcounty.gov/assessor/DataDownload/
Create data for 'hed' approach
Description
Generate standardized data for the 'hed' modeling approach
Usage
hedCreateTrans(trans_df, prop_id, trans_id, price, date = NULL,
periodicity = NULL, ...)
Arguments
trans_df |
sales transaction in either a data.frame or a trans_df class from dateToPeriod() function |
prop_id |
field contain the unique property identification |
trans_id |
field containing the unique transaction identification |
price |
field containing the transaction price |
date |
default=NULL, field containing the date of the transaction. Only necessary if not passing an 'hpidata' object |
periodicity |
default=NULL, field containing the desired periodicity of analysis. Only necessary if not passing a 'hpidata' object |
... |
Additional arguments |
Value
data.frame of transactions with standardized period field. Note that a full data.frame of the possible periods, their values and names can be found in the attributes to the returned 'hed' object
Examples
# Load example data
data(ex_sales)
# Create Hed Data
ex_heddata <- hedCreateTrans(trans_df = ex_sales,
prop_id = 'pinx',
trans_id = 'sale_id',
price = 'sale_price',
date = 'sale_date',
periodicity = 'monthly')
Create a full index object by hedonic approach
Description
Wrapper to create index object via entire hedonic approach
Usage
hedIndex(trans_df, dep_var = NULL, ind_var = NULL, hed_spec = NULL,
...)
Arguments
trans_df |
data.frame of transactions |
dep_var |
default = NULL; Dependent variable in hedonic model |
ind_var |
default = NULL; Independent variables in the hedonic model |
hed_spec |
default = NULL; Full hedonic model specification |
... |
Additional Arguments |
Value
'hpi“ object. S3 list with:
- data
'hpidata' object
- model
'hpimodel' object
- index
'hpiindex' object
Further Details
Additional argument need to provide necessary argument for create 'hpidata' objects if the 'trans_df' object is not of that class.
Examples
# Load data
data(ex_sales)
# Create index with raw transaction data
hed_index <- hedIndex(trans_df = ex_sales,
periodicity = 'monthly',
min_date = '2010-06-01',
max_date = '2015-11-30',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'robust',
log_dep = TRUE,
trim_model = TRUE,
max_period = 48,
dep_var = 'price',
ind_var = c('tot_sf', 'beds', 'baths'),
smooth = FALSE)
Estimate hedonic model for index creation
Description
Estimate coefficients for an index via the hedonic approach (generic method)
Usage
hedModel(estimator, hed_df, hed_spec, ...)
Arguments
estimator |
Type of model to estimates (base, robust, weighted) |
hed_df |
Repeat sales dataset from hedCreateSales() |
hed_spec |
Model specification ('formula' object) |
... |
Additional arguments |
Value
'hedmodel' object: model object of the estimator (ex.: 'lm')
Further Details
‘estimator' argument must be in a class of ’base', 'weighted' or 'robust' This function is not generally called directly, but rather from 'hpiModel()'
Examples
# Load example data
data(ex_sales)
# Create hedonic data
hed_data <- hedCreateTrans(trans_df = ex_sales,
prop_id = 'pinx',
trans_id = 'sale_id',
price = 'sale_price',
date = 'sale_date',
periodicity = 'monthly')
# Estimate Model
hed_model <- hedModel(estimator = structure('base', class = 'base'),
hed_df = hed_data,
hed_spec = as.formula(log(price) ~ baths + tot_sf))
Hedonic model approach with base estimator
Description
Use of base estimator in hedonic model approach
Usage
## S3 method for class 'base'
hedModel(estimator, hed_df, hed_spec, ...)
Arguments
estimator |
Type of model to estimates (base, robust, weighted) |
hed_df |
Repeat sales dataset from hedCreateSales() |
hed_spec |
Model specification ('formula' object) |
... |
Additional arguments |
Further Details
See '?hedModel' for more information
Hedonic model approach with robust estimator
Description
Use of robust estimator in hedonic model approach
Usage
## S3 method for class 'robust'
hedModel(estimator, hed_df, hed_spec, ...)
Arguments
estimator |
Type of model to estimates (base, robust, weighted) |
hed_df |
Repeat sales dataset from hedCreateSales() |
hed_spec |
Model specification ('formula' object) |
... |
Additional arguments |
Further Details
See '?hedModel' for more information
See '?hedModel' for more information
Hedonic model approach with weighted estimator
Description
Use of weighted estimator in hedonic model approach
Usage
## S3 method for class 'weighted'
hedModel(estimator, hed_df, hed_spec, ...)
Arguments
estimator |
Type of model to estimates (base, robust, weighted) |
hed_df |
Repeat sales dataset from hedCreateSales() |
hed_spec |
Model specification ('formula' object) |
... |
Additional arguments |
Further Details
See '?hedModel' for more information
Wrapper to estimate model approaches (generic method)
Description
Generic method to estimate modeling approaches for indexes
Usage
hpiModel(model_type, hpi_df, estimator = "base", log_dep = TRUE,
trim_model = TRUE, mod_spec = NULL, ...)
Arguments
model_type |
Type of model to estimate ('rt', 'hed', 'rf') |
hpi_df |
Dataset created by one of the *CreateTrans() function in this package. |
estimator |
Type of estimator to be used ('base', 'weighted', 'robust') |
log_dep |
default TRUE, should the dependent variable (change in price) be logged? |
trim_model |
default TRUE, should excess be trimmed from model results ('lm' or 'rlm' object)? |
mod_spec |
Model specification |
... |
Additional Arguments |
Value
hpimodel object consisting of:
- estimator
Type of estimator
- coefficients
Data.frame of coefficient
- model_obj
class 'rtmodel' or 'hedmodel'
- mod_spec
Full model specification
- log_dep
Binary: is the dependent variable in logged format
- base_price
Mean price in the base period
- periods
'data.frame' of periods
- approach
Type of model used
Examples
# Load data
data(ex_sales)
# With a raw transaction data.frame
rt_data <- rtCreateTrans(trans_df = ex_sales,
prop_id = 'pinx',
trans_id = 'sale_id',
price = 'sale_price',
periodicity = 'monthly',
date = 'sale_date')
# Create model object
hpi_model <- hpiModel(model_type = 'rt',
hpi_df = rt_data,
estimator = 'base',
log_dep = TRUE)
# For custom weighted repeat transaction model
hpi_model_wgt <- hpiModel(model_type = 'rt',
hpi_df = rt_data,
estimator = 'weighted',
weights = runif(nrow(rt_data), 0, 1))
Specific method for hpi modeling (hed) approach)
Description
Estimate hpi models with hed approach
Usage
## S3 method for class 'hed'
hpiModel(model_type, hpi_df, estimator = "base",
log_dep = TRUE, trim_model = TRUE, mod_spec = NULL,
dep_var = NULL, ind_var = NULL, ...)
Arguments
model_type |
Type of model to estimate ('rt', 'hed', 'rf') |
hpi_df |
Dataset created by one of the *CreateSales() function in this package. |
estimator |
Type of estimator to be used ('base', 'weighted', 'robust') |
log_dep |
default=TRUE; should the dependent variable (change in price) be logged? |
trim_model |
default TRUE, should excess be trimmed from model results ('lm' or 'rlm' object)? |
mod_spec |
default=NULL; hedonic model specification |
dep_var |
default=NULL; dependent variable of the model |
ind_var |
default=NULL; independent variable(s) of the model |
... |
Additional Arguments |
Value
hpimodel object consisting of:
- estimator
Type of estimator
- coefficients
Data.frame of coefficient
- model_obj
class 'rtmodel' or 'hedmodel'
- mod_spec
Full model specification
- log_dep
Binary: is the dependent variable in logged format
- base_price
Mean price in the base period
- periods
'data.frame' of periods
- approach
Type of model used
Specific method for hpi modeling (hed) approach)
Description
Estimate hpi models with hed approach
Usage
## S3 method for class 'rf'
hpiModel(model_type, hpi_df, estimator = "pdp",
log_dep = TRUE, trim_model = TRUE, mod_spec = NULL,
dep_var = NULL, ind_var = NULL, ...)
Arguments
model_type |
Type of model ('rt', 'hed', 'rf') |
hpi_df |
Dataset created by one of the *CreateSales() function in this package. |
estimator |
Type of estimator to be used ('base', 'weighted', 'robust') |
log_dep |
default=TRUE; should the dependent variable (change in price) be logged? |
trim_model |
default TRUE, should excess be trimmed from model results ('lm' or 'rlm' object)? |
mod_spec |
default=NULL; hedonic model specification |
dep_var |
default=NULL; dependent variable of the model |
ind_var |
default=NULL; independent variable(s) of the model |
... |
Additional Arguments |
Value
hpimodel object consisting of:
- estimator
Type of estimator
- coefficients
Data.frame of coefficient
- model_obj
class 'rtmodel' or 'hedmodel'
- mod_spec
Full model specification
- log_dep
Binary: is the dependent variable in logged format
- base_price
Mean price in the base period
- periods
'data.frame' of periods
- approach
Type of model used
Specific method for hpi modeling (rt approach)
Description
Estimate hpi models with rt approach
Usage
## S3 method for class 'rt'
hpiModel(model_type, hpi_df, estimator = "base",
log_dep = TRUE, trim_model = TRUE, mod_spec = NULL, ...)
Arguments
model_type |
Type of model to estimate ('rt', 'hed', 'rf') |
hpi_df |
Dataset created by one of the *CreateTrans() function in this package. |
estimator |
Type of estimator to be used ('base', 'weighted', 'robust') |
log_dep |
default TRUE, should the dependent variable (change in price) be logged? |
trim_model |
default TRUE, should excess be trimmed from model results ('lm' or 'rlm' object)? |
mod_spec |
Model specification |
... |
Additional Arguments |
Value
hpimodel object consisting of:
- estimator
Type of estimator
- coefficients
Data.frame of coefficient
- model_obj
class 'rtmodel' or 'hedmodel'
- mod_spec
Full model specification
- log_dep
Binary: is the dependent variable in logged format
- base_price
Mean price in the base period
- periods
'data.frame' of periods
- approach
Type of model used
Helper function to make KFold data
Description
Function to help create KFold data based on approach (Generic Method)
Usage
matchKFold(train_df, pred_df)
Arguments
train_df |
Data.frame of training data |
pred_df |
Data.frame (class 'hpidata“) to be used for prediction |
Value
list
- train
Training data
- score
Scoring data
Further Details
Helper function called from createKFoldData
Helper function to make KFold data
Description
Function to help create KFold data based on hed approach
Usage
## S3 method for class 'heddata'
matchKFold(train_df, pred_df)
Arguments
train_df |
Data.frame of training data |
pred_df |
Data.frame (class 'hpidata“) to be used for prediction |
Helper function to make KFold data
Description
Function to help create KFold data based on rt approach
Usage
## S3 method for class 'rtdata'
matchKFold(train_df, pred_df)
Arguments
train_df |
Data.frame of training data |
pred_df |
Data.frame (class 'hpidata“) to be used for prediction |
Convert model results into a house price index
Description
Converts model results to standardized index objects
Usage
modelToIndex(model_obj, max_period = max(model_obj$coefficients$time),
...)
Arguments
model_obj |
Model results object |
max_period |
Maximum number of periods that should have been estimated. |
... |
Additional arguments |
Value
'hpiindex' object containing:
name |
vector of period names |
numeric |
vector of period in numeric form |
period |
vector of period numbers |
value |
'ts' object of the index values |
imputed |
vector of binary values indicating imputation |
Examples
# Load data
data(ex_sales)
# With a raw transaction data.frame
rt_data <- rtCreateTrans(trans_df = ex_sales,
prop_id = 'pinx',
trans_id = 'sale_id',
price = 'sale_price',
periodicity = 'monthly',
date = 'sale_date')
# Create model object
hpi_model <- hpiModel(model_type = 'rt',
hpi_df = rt_data,
estimator = 'base',
log_dep = TRUE)
# Create Index
hpi_index <- modelToIndex(hpi_model,
max_period = 84)
Plot method for 'hpi' object
Description
Specific plotting method for hpi objects
Usage
## S3 method for class 'hpi'
plot(x, ...)
Arguments
x |
Object to plot of class 'hpi' |
... |
Additional Arguments |
Value
'plotindex' object inheriting from a ggplot object
Further Details
Additional argument can include those argument for 'plot.hpindex“
Examples
# Load data
data(ex_sales)
# Create index with raw transaction data
rt_index <- rtIndex(trans_df = ex_sales,
periodicity = 'monthly',
min_date = '2010-06-01',
max_date = '2015-11-30',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'robust',
log_dep = TRUE,
trim_model = TRUE,
max_period = 48,
smooth = FALSE)
# Plot data
plot(rt_index)
plot(rt_index, smooth = TRUE)
Plot method for 'hpiaccuracy' object
Description
Specific plotting method for hpiaccuracy objects
Usage
## S3 method for class 'hpiaccuracy'
plot(x, return_plot = FALSE, do_plot = TRUE,
use_log_error = FALSE, ...)
Arguments
x |
Object to plot of class 'hpiaccuracy“ |
return_plot |
default = FALSE; Return the plot to the function call |
do_plot |
default = FALSE; Execute plotting to terminal/console |
use_log_error |
[FALSE] Use the log error? |
... |
Additional Arguments |
Value
'plotaccuracy' object inheriting from a ggplot object
Examples
# Load Data
data(ex_sales)
# Create Index
rt_index <- rtIndex(trans_df = ex_sales,
periodicity = 'monthly',
min_date = '2010-06-01',
max_date = '2015-11-30',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'robust',
log_dep = TRUE,
trim_model = TRUE,
max_period = 48,
smooth = FALSE)
# Calculate insample accuracy
hpi_accr <- calcAccuracy(hpi_obj = rt_index,
test_type = 'rt',
test_method = 'insample')
# Make Plot
plot(hpi_accr)
Plot method for 'hpiindex' object
Description
Specific plotting method for hpiindex objects
Usage
## S3 method for class 'hpiindex'
plot(x, show_imputed = FALSE, smooth = FALSE, ...)
Arguments
x |
Object to plot of class 'hpiindex“ |
show_imputed |
default = FALSE; highlight the imputed points |
smooth |
default = FALSE; plot the smoothed index |
... |
Additional Arguments |
Value
'plotindex' object inheriting from a ggplot object
Examples
# Load data
data(ex_sales)
# With a raw transaction data.frame
rt_data <- rtCreateTrans(trans_df = ex_sales,
prop_id = 'pinx',
trans_id = 'sale_id',
price = 'sale_price',
periodicity = 'monthly',
date = 'sale_date')
# Create model object
hpi_model <- hpiModel(model_type = 'rt',
hpi_df = rt_data,
estimator = 'base',
log_dep = TRUE)
# Create Index
hpi_index <- modelToIndex(hpi_model,
max_period = 84)
# Make Plot
plot(hpi_index)
Plot method for 'indexvolatility' object
Description
Specific plotting method for indexvolatility objects
Usage
## S3 method for class 'indexvolatility'
plot(x, ...)
Arguments
x |
Object to plot of class 'indexvolatility“ |
... |
Additional Arguments |
Value
'plotvolatility' object inheriting from a ggplot object
Examples
# Load Data
data(ex_sales)
# Create index with raw transaction data
rt_index <- rtIndex(trans_df = ex_sales,
periodicity = 'monthly',
min_date = '2010-06-01',
max_date = '2015-11-30',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'robust',
log_dep = TRUE,
trim_model = TRUE,
max_period = 48,
smooth = FALSE)
# Calculate Volatility
index_vol <- calcVolatility(index = rt_index,
window = 3)
# Make Plot
plot(index_vol)
Plot method for 'seriesaccuracy' object
Description
Specific plotting method for seriesaccuracy objects
Usage
## S3 method for class 'seriesaccuracy'
plot(x, return_plot = FALSE, ...)
Arguments
x |
Object of class 'hpiaccuracy“ |
return_plot |
default = FALSE; Return the plot to the function call |
... |
Additional argument (passed to 'plot.hpiaccuracy()“) |
Value
'plotaccuracy' object inheriting from a ggplot object
Examples
# Load data
data(ex_sales)
# Create index
rt_index <- rtIndex(trans_df = ex_sales,
periodicity = 'monthly',
min_date = '2010-06-01',
max_date = '2015-11-30',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'robust',
log_dep = TRUE,
trim_model = TRUE,
max_period = 48,
smooth = FALSE)
# Create Series (Suppressing messages do to small sample size of this example)
suppressMessages(
hpi_series <- createSeries(hpi_obj = rt_index,
train_period = 12))
# Calculate insample accuracy
hpi_series_accr <- calcSeriesAccuracy(series_obj = hpi_series,
test_type = 'rt',
test_method = 'insample')
# Make Plot
plot(hpi_series_accr)
Plot method for 'serieshpi' object
Description
Specific plotting method for serieshpi objects
Usage
## S3 method for class 'serieshpi'
plot(x, smooth = FALSE, ...)
Arguments
x |
Object of class 'serieshpi' |
smooth |
default = FALSE; plot the smoothed object |
... |
Additional Arguments' |
Value
'plotseries' object inheriting from a ggplot object
Examples
# Load data
data(ex_sales)
# Create index
rt_index <- rtIndex(trans_df = ex_sales,
periodicity = 'monthly',
min_date = '2010-06-01',
max_date = '2015-11-30',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'robust',
log_dep = TRUE,
trim_model = TRUE,
max_period = 48,
smooth = FALSE)
# Create Series (Suppressing messages do to small sample size of this example)
suppressMessages(
hpi_series <- createSeries(hpi_obj = rt_index,
train_period = 12))
# Make Plot
plot(hpi_series)
Plot method for 'seriesrevision' object
Description
Specific plotting method for seriesrevision objects
Usage
## S3 method for class 'seriesrevision'
plot(x, measure = "median", ...)
Arguments
x |
Object to plot of class 'seriesrevision' |
measure |
default = 'median'; Metric to plot ('median' or 'mean') |
... |
Additional Arguments |
Value
'plotrevision' object inheriting from a ggplot object
Examples
# Load example sales
data(ex_sales)
# Create Index
rt_index <- rtIndex(trans_df = ex_sales,
periodicity = 'monthly',
min_date = '2010-06-01',
max_date = '2015-11-30',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'robust',
log_dep = TRUE,
trim_model = TRUE,
max_period = 48,
smooth = FALSE)
# Create Series (Suppressing messages do to small sample size of this example)
suppressMessages(
hpi_series <- createSeries(hpi_obj = rt_index,
train_period = 12))
# Calculate revision
series_rev <- calcRevision(series_obj = hpi_series)
# Make Plot
plot(series_rev)
Create a full index object by random forest approach
Description
Wrapper to create index object via entire random forest approach
Usage
rfIndex(trans_df, dep_var = NULL, ind_var = NULL, rf_spec = NULL,
...)
Arguments
trans_df |
data.frame of transactions |
dep_var |
default = NULL; Dependent variable in hedonic model |
ind_var |
default = NULL; Independent variables in the hedonic model |
rf_spec |
default = NULL; Full random forest model specification |
... |
Additional Arguments |
Value
'hpi“ object. S3 list with:
- data
'hpidata' object
- model
'hpimodel' object
- index
'hpiindex' object
Further Details
Additional argument need to provide necessary argument for create 'hpidata' objects if the 'trans_df' object is not of that class.
Examples
# Load data
data(ex_sales)
# Create index with raw transaction data
rf_index <- rfIndex(trans_df = ex_sales,
periodicity = 'monthly',
min_date = '2010-06-01',
max_date = '2015-11-30',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'pdp',
log_dep = TRUE,
trim_model = TRUE,
max_period = 48,
dep_var = 'price',
ind_var = c('tot_sf', 'beds', 'baths'),
smooth = FALSE,
ntrees = 10,
sim_count = 2)
Estimate random forest model for index creation
Description
Estimate coefficients for an index via the random forest approach (generic method)
Usage
rfModel(estimator, rf_df, rf_spec, ntrees = 200, seed = 1, ...)
Arguments
estimator |
Type of model to estimates (pdp) |
rf_df |
Transactions dataset from hedCreateSales() |
rf_spec |
Model specification ('formula' object) |
ntrees |
[200] Set number of trees to use |
seed |
[1] Random seed for reproducibility |
... |
Additional arguments |
Value
'rfmodel' object: model object of the estimator (ex.: 'lm')
Further Details
‘estimator' argument must be in a class of ’pdp' This function is not generally called directly, but rather from 'hpiModel()'
Examples
# Load example data
data(ex_sales)
# Create hedonic data
hed_data <- hedCreateTrans(trans_df = ex_sales,
prop_id = 'pinx',
trans_id = 'sale_id',
price = 'sale_price',
date = 'sale_date',
periodicity = 'monthly')
# Estimate Model
rf_model <- rfModel(estimator = structure('pdp', class = 'pdp'),
rf_df = hed_data,
rf_spec = as.formula(log(price) ~ baths + tot_sf),
ntrees = 10,
sim_count = 1)
Random forest model approach with pdp estimator
Description
Use of pdp estimator in random forest approach
Usage
## S3 method for class 'pdp'
rfModel(estimator, rf_df, rf_spec, ntrees = 200,
seed = 1, ...)
Arguments
estimator |
Type of model to estimates (pdp) |
rf_df |
Transactions dataset from hedCreateSales() |
rf_spec |
Model specification ('formula' object) |
ntrees |
[200] Set number of trees to use |
seed |
[1] Random seed for reproducibility |
... |
Additional arguments |
Further Details
See '?rfModel' for more information
Create simulation data for Random forest approach
Description
Create data to use in PDP simulation
Usage
rfSimDf(rf_df, seed, sim_ids = NULL, sim_count = NULL,
sim_per = NULL, ...)
Arguments
rf_df |
Full training dataset |
seed |
Random seed for reproducibility |
sim_ids |
row ids to simulate |
sim_count |
number of random rows to simulate |
sim_per |
percent of rows to randomly simulate |
... |
Additional arguments |
Further Details
See '?rfModel' for more information
Create transaction data for rt approach
Description
Generate standardized object for rt estimate approach
Usage
rtCreateTrans(trans_df, prop_id, trans_id, price, date = NULL,
periodicity = NULL, seq_only = FALSE, min_period_dist = NULL, ...)
Arguments
trans_df |
transactions in either a data.frame or a 'hpidata“ class from dateToPeriod() function |
prop_id |
field contain the unique property identification |
trans_id |
field containing the unique transaction identification |
price |
field containing the transaction price |
date |
default=NULL, field containing the date of the sale. Only necessary if not passing an 'hpidata' object |
periodicity |
default=NULL, field containing the desired periodicity of analysis. Only necessary if not passing a 'hpidata' object |
seq_only |
default=FALSE, indicating whether to only include sequential repeat observations 1 to 2 and 2 to 3. False returns 1 to 2, 1 to 3 and 2 to 3. |
min_period_dist |
[12] Minimum number of period required between repeat sales |
... |
Additional arguments |
Value
data.frame of repeat transactions. Note that a full data.frame of the possible periods, their values and names can be found in the attributes to the returned 'rtdata' object
Further Details
Properties with greater than two transactions during the period will make pairwise matches among all sales. Any property transacting twice in the same period will remove the lower priced of the two transactions. If passing a raw data.frame (not a 'hpidata“ object) the "date" field should refer to a field containing a vector of class POSIXt or Date.
Examples
# Load data
data(ex_sales)
# With a raw transaction data.frame
rt_data <- rtCreateTrans(trans_df = ex_sales,
prop_id = 'pinx',
trans_id = 'sale_id',
price = 'sale_price',
periodicity = 'monthly',
date = 'sale_date')
Create a full index object by repeat transaction approach
Description
Wrapper to create index object via entire repeat transaction approach
Usage
rtIndex(trans_df, ...)
Arguments
trans_df |
data.frame of transactions. Can be a 'hpidata' or an 'rtdata' object. |
... |
Additional Arguments |
Value
'hpi“ object. S3 list with:
- data
'hpidata' object
- model
'hpimodel' object
- index
'hpiindex' object
Further Details
Additional argument need to provide necessary argument for create 'hpidata' objects if the 'trans_df' object is not of that class.
Examples
# Load data
data(ex_sales)
# Create index with raw transaction data
rt_index <- rtIndex(trans_df = ex_sales,
periodicity = 'monthly',
min_date = '2010-06-01',
max_date = '2015-11-30',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'robust',
log_dep = TRUE,
trim_model = TRUE,
max_period = 48,
smooth = FALSE)
Estimate repeat transaction model for index creation
Description
Estimate coefficients for an index via the repeat transaction approach (generic method)
Usage
rtModel(rt_df, time_matrix, price_diff, estimator, lm_recover = TRUE,
...)
Arguments
rt_df |
Repeat transactions dataset from rtCreateTrans() |
time_matrix |
Time matrix object from rtTimeMatrix() |
price_diff |
Difference in price between the two transactions |
estimator |
Type of model to estimates (base, robust, weighted). Must be in that class. |
lm_recover |
(TRUE) Allows robust model to use linear model if it fails |
... |
Additional arguments |
Value
'rtmodel' object
Further Details
Three available specific methods: 'base', 'robust' and 'weighted'
Examples
# Load data
data(ex_sales)
# With a raw transaction data.frame
rt_data <- rtCreateTrans(trans_df = ex_sales,
prop_id = 'pinx',
trans_id = 'sale_id',
price = 'sale_price',
periodicity = 'monthly',
date = 'sale_date')
# Calc price differences
price_diff <- rt_data$price_2 - rt_data$price_1
# Create time matrix
rt_matrix <- rtTimeMatrix(rt_data)
# Calculate model
rt_model <- rtModel(rt_df = rt_data,
price_diff = price_diff,
time_matrix = rt_matrix,
estimator = structure('base', class='base'))
Repeat transaction model approach with base estimator
Description
Use of base estimator in repeat transactions model approach
Usage
## S3 method for class 'base'
rtModel(rt_df, time_matrix, price_diff, estimator, ...)
Arguments
rt_df |
Repeat transactions dataset from rtCreateTrans() |
time_matrix |
Time matrix object from rtTimeMatrix() |
price_diff |
Difference in price between the two transactions |
estimator |
Type of model to estimates (base, robust, weighted). Must be in that class. |
... |
Additional arguments |
Further Details
See '?rtModel' for more information
Repeat transaction model approach with robust estimator
Description
Use of robust estimator in repeat transactions model approach
Usage
## S3 method for class 'robust'
rtModel(rt_df, time_matrix, price_diff, estimator,
lm_recover = TRUE, ...)
Arguments
rt_df |
Repeat transactions dataset from rtCreateTrans() |
time_matrix |
Time matrix object from rtTimeMatrix() |
price_diff |
Difference in price between the two transactions |
estimator |
Type of model to estimates (base, robust, weighted). Must be in that class. |
lm_recover |
(TRUE) Allows robust model to use linear model if it fails |
... |
Additional arguments |
Further Details
See '?rtModel' for more information
Repeat transaction model approach with weighted estimator
Description
Use of weighted estimator in repeat transactions model approach
Usage
## S3 method for class 'weighted'
rtModel(rt_df, time_matrix, price_diff, estimator, ...)
Arguments
rt_df |
Repeat transactions dataset from rtCreateTrans() |
time_matrix |
Time matrix object from rtTimeMatrix() |
price_diff |
Difference in price between the two transactions |
estimator |
Type of model to estimates (base, robust, weighted). Must be in that class. |
... |
Additional arguments |
Further Details
See '?rtModel' for more information
Create model matrix for repeat transaction approach
Description
Generates the array necessary to estimate a repeat transactions model
Usage
rtTimeMatrix(rt_df)
Arguments
rt_df |
object of class 'rtdata': repeat transaction data.frame created by rtCreateTrans() |
Value
matrix to be used on the right hand side of a repeat sales regression model
Further Details
Time periods are calculated from the data provided.
Examples
# Load data
data(ex_sales)
# With a raw transaction data.frame
rt_data <- rtCreateTrans(trans_df = ex_sales,
prop_id = 'pinx',
trans_id = 'sale_id',
price = 'sale_price',
periodicity = 'monthly',
date = 'sale_date')
# Create Matrix
rt_matrix <- rtTimeMatrix(rt_data)
Seattle Home Sales
Description
Seattle home sales from 2010 to 2016. Includes only detached single family residences and townhomes. Data gathered from the King County Assessor's FTP site. A number of initial data munging tasks were necessary to bring the data into this format.
Usage
data(seattle_sales)
Format
A "data.frame" with 43,313 rows and 16 variables
- pinx
The unique property identifying code. Original value is preceded by two '..'s to prevent the dropping of leading zeros
- sale_id
The unique transaction identifying code.
- sale_price
Price of the home
- sale_date
Date of sale
- use_type
Property use type
- area
Assessment area or zone
- lot_sf
Size of lot in square feet
- wfnt
Is property waterfront?
- bldg_grade
Quality of the building construction (higher is better)
- tot_sf
Size of home in square feet
- beds
Number of bedrooms
- baths
Number of bathrooms
- age
Age of home
- eff_age
Age of home, considering major remodels
- longitude
Longitude
- latitude
Latitude
Source
King County Assessor: http://info.kingcounty.gov/assessor/DataDownload/
Smooth an index
Description
Smooths an existing hpiindex object
Usage
smoothIndex(index_obj, order = 3, in_place = FALSE, ...)
Arguments
index_obj |
Index to be smoothed |
order |
default = 3; Number of nearby period to smooth with, multiple means multiple iterations |
in_place |
default = FALSE; adds smoothed index to the 'hpiindex' object |
... |
Additional Arguments |
Value
a ‘ts“ and ’smooth_index' object with smoothed index
Further Details
Leaving order blank default to a moving average with order 3.
Examples
# Load data
data(ex_sales)
# Create index with raw transaction data
rt_index <- rtIndex(trans_df = ex_sales,
periodicity = 'monthly',
min_date = '2010-06-01',
max_date = '2015-11-30',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'robust',
log_dep = TRUE,
trim_model = TRUE,
max_period = 48,
smooth = FALSE)
# Create Smooth index
sm_index <- smoothIndex(index_obj = rt_index,
order = 3,
in_place = FALSE)
# Create Smooth index (in place)
sm_index <- smoothIndex(index_obj = rt_index,
order = 3,
in_place = TRUE)
Smooth all indexes in a series
Description
Smooths all indexes within a progressive series of indexes
Usage
smoothSeries(series_obj, order = 3, ...)
Arguments
series_obj |
Series to be smoothed |
order |
Number of nearby period to smooth with |
... |
Additional Arguments |
Value
a 'serieshpi' object with a smoothed index in each 'hpiindex' object
Further Details
Leaving order blank default to a moving average with order 3.
Examples
# Load data
data(ex_sales)
# Create index
rt_index <- rtIndex(trans_df = ex_sales,
periodicity = 'monthly',
min_date = '2010-06-01',
max_date = '2015-11-30',
adj_type = 'clip',
date = 'sale_date',
price = 'sale_price',
trans_id = 'sale_id',
prop_id = 'pinx',
estimator = 'robust',
log_dep = TRUE,
trim_model = TRUE,
max_period = 48,
smooth = FALSE)
# Create Series (Suppressing messages do to small sample size of this example)
suppressMessages(
hpi_series <- createSeries(hpi_obj = rt_index,
train_period = 12))
# Smooth indexes
sm_series <- smoothSeries(series_obj = hpi_series,
order = 5)