Package 'cbsREPS'

Calculate direct index according to the Fisher hedonic double imputation method

Description

By the parameters 'dependent_variable', 'continue_variable' and 'categorical_variables' as regression model is compiled. With the model, a direct series of index figures is estimated by use of hedonic regression.

Usage

calculate_fisher(
  dataset,
  period_variable,
  dependent_variable,
  continuous_variables,
  categorical_variables,
  reference_period = NULL,
  number_of_observations = FALSE
)

Arguments

dataset	table with data (does not need to be a selection of relevant variables)
period_variable	variable in the table with periods
dependent_variable	usually the sale price
continuous_variables	vector with quality determining numeric variables (no dummies)
categorical_variables	vector with quality determining categorical variables (also dummies)
reference_period	period or group of periods that will be set to 100 (numeric/string)
number_of_observations	number of observations per period (default = TRUE)

Details

N.B.: the independent variables must be entered transformed (and ready) in the parameters. Hence, not: log(floor_area), but transform the variable in advance and then provide log_floor_area. This does not count for the dependent variable. This should be entered untransformed

Within the data, it is not neccesary to filter the data on relevant variables or complete records. This is taken care of in the function.

Value

table with index, imputation averages, number of observations and confidence intervals per period

Author(s)

Farley Ishaak

---

Calculate the geometric average of a series of values

Description

The equation for the calculation is:: exp(mean(log(series_values)))

Usage

calculate_geometric_average(values)

Arguments

values

series with numeric values

Value

geometric average

Author(s)

Farley Ishaak

---

Calculate direct index according to the Laspeyres hedonic double imputation method

Description

By the parameters 'dependent_variable', 'continue_variable' and 'categorical_variables' as regression model is compiled. With the model, a direct series of index figures is estimated by use of hedonic regression.

Usage

calculate_laspeyres(
  dataset,
  period_variable,
  dependent_variable,
  continuous_variables,
  categorical_variables,
  reference_period = NULL,
  index = TRUE,
  number_of_observations = FALSE,
  imputation = FALSE
)

Arguments

dataset	table with data (does not need to be a selection of relevant variables)
period_variable	variable in the table with periods
dependent_variable	usually the sale price
continuous_variables	vector with quality determining numeric variables (no dummies)
categorical_variables	vector with quality determining categorical variables (also dummies)
reference_period	period or group of periods that will be set to 100 (numeric/string)
index	caprice index
number_of_observations	number of observations per period (default = TRUE)
imputation	display the underlying average imputation values? (default = FALSE)

Details

N.B.: the independent variables must be entered transformed (and ready) in the parameters. Hence, not: log(floor_area), but transform the variable in advance and then provide log_floor_area. This does not count for the dependent variable. This should be entered untransformed/

Within the data, it is not necessary to filter the data on relevant variables or complete records. This is taken care of in the function.

Value

table with index, imputation averages, number of observations and confidence intervals per period

Author(s)

Farley Ishaak

---

Calculate direct index according to the Paasche hedonic double imputation method

Description

By the parameters 'dependent_variable', 'continue_variable' and 'categorical_variables' as regression model is compiled. With the model, a direct series of index figures is estimated by use of hedonic regression.

Usage

calculate_paasche(
  dataset,
  period_variable,
  dependent_variable,
  continuous_variables,
  categorical_variables,
  reference_period = NULL,
  index = TRUE,
  number_of_observations = FALSE,
  imputation = FALSE
)

Arguments

dataset	table with data (does not need to be a selection of relevant variables)
period_variable	variable in the table with periods
dependent_variable	usually the sale price
continuous_variables	vector with quality determining numeric variables (no dummies)
categorical_variables	vector with quality determining categorical variables (also dummies)
reference_period	period or group of periods that will be set to 100 (numeric/string)
index	caprice index
number_of_observations	number of observations per period (default = TRUE)
imputation	display the underlying average imputation values? (default = FALSE)

Details

N.B.: the independent variables must be entered transformed (and ready) in the parameters. Hence, not: log(floor_area), but transform the variable in advance and then provide log_floor_area. This does not count for the dependent variable. This should be entered untransformed

Within the data, it is not necessary to filter the data on relevant variables or complete records. This is taken care of in the function.

Value

table with index, imputation averages, number of observations and confidence intervals per period

Author(s)

Farley Ishaak

---

Calculate index based on specified method (Fisher, Laspeyres, Paasche, HMTS)

Description

Central hub function to calculate index figures using different methods.

Usage

calculate_price_index(
  method,
  dataset,
  period_variable,
  dependent_variable,
  continuous_variables,
  categorical_variables,
  reference_period = NULL,
  number_of_observations = TRUE,
  periods_in_year = 4,
  production_since = NULL,
  number_preliminary_periods = 3,
  resting_points = FALSE,
  index = TRUE,
  imputation = FALSE
)

Arguments

method	one of: "fisher", "laspeyres", "paasche", "hmts"
dataset	data frame with input data
period_variable	name of the variable indicating time periods
dependent_variable	usually the price
continuous_variables	vector with numeric quality-determining variables
categorical_variables	vector with categorical variables (also dummies)
reference_period	period or group of periods that will be set to 100
number_of_observations	show number of observations? Default = TRUE
periods_in_year	(HMTS only) number of periods per year (e.g. 12 for months)
production_since	(HMTS only) start period for production simulation
number_preliminary_periods	(HMTS only) number of preliminary periods
resting_points	(HMTS only) return detailed outputs? Default = FALSE
index	(Laspeyres/Paasche only) include index column? Default = TRUE
imputation	(Laspeyres/Paasche only) include imputation values? Default = FALSE

Value

A data.frame (or list for when method is HMTS with resting_points = TRUE)

Author(s)

Vivek Gajadhar

Examples

# Laspeyres index
Tbl_Laspeyres <- calculate_price_index(
  method = "laspeyres",
  dataset = data_constraxion,
  period_variable = "period",
  dependent_variable = "price",
  continuous_variables = "floor_area",
  categorical_variables = "neighbourhood_code",
  reference_period = 2015,
  number_of_observations = TRUE,
  imputation = FALSE
)
head(Tbl_Laspeyres)

# Paasche index
Tbl_Paasche <- calculate_price_index(
  method = "paasche",
  dataset = data_constraxion,
  period_variable = "period",
  dependent_variable = "price",
  continuous_variables = "floor_area",
  categorical_variables = "neighbourhood_code",
  reference_period = 2015,
  number_of_observations = TRUE,
  imputation = FALSE
)
head(Tbl_Paasche)

# Fisher index (geometric mean of Laspeyres and Paasche)
Tbl_Fisher <- calculate_price_index(
  method = "fisher",
  dataset = data_constraxion,
  period_variable = "period",
  dependent_variable = "price",
  continuous_variables = "floor_area",
  categorical_variables = "neighbourhood_code",
  reference_period = 2015,
  number_of_observations = TRUE
)
head(Tbl_Fisher)

---

Default update function

Description

This function is used in the function: calculate_trend_line_KFAS()

Usage

custom_update_function(params, model)

Arguments

params	startvalues
model	state space modelnumber

Value

Newmodel

Author(s)

Vivek Gajadhar

---

A real estate example dataframe

Description

A subset of data from a fictitious real estate data frame containing transaction prices and some categorical and numerical characteristics of each dwelling.

Usage

data_constraxion

Format

A data frame with 7,800 rows and 6 columns:

period

A (string) vector indicating a time period

price

A (string) vector indicating the transaction price of the dwelling

floor_area

A real-valued vector of (the logarithm of) the floor area of the dwelling

dist_trainstation

A real-valued vector of (the logarithm of) the distance of the dwelling to the nearest train station

neighbourhood_code

A categorical code/string referring to the neighbourhood the dwelling belongs to

dummy_large_city

A vector indicating whether the dwelling belongs to a large city or not

Source

A fictitious dataset for illustration purposes

Examples

data(data_constraxion)
head(data_constraxion)

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