#global parameters for knit knitr::opts_chunk$set(echo = FALSE) knitr::opts_chunk$set(warning = FALSE) knitr::opts_chunk$set(message = FALSE) knitr::opts_chunk$set(fig.align = "center") knitr::opts_chunk$set(fig.height = 3) #knitr::opts_chunk$set(fig.width = 8) knitr::opts_chunk$set(out.width = "90%") #knitr::opts_chunk$set(out.height = "100%") #R package dependencies library(devtools) library(ggplot2) library(rio) library(dplyr) library(plotly) library(forcats) library(DiagrammeR) library(lubridate) library(ggrepel) library(stringr) library(DT) library(OECDHousingToolkit) library(downloadthis) library(htmlwidgets) #download the functions necessary for the simulator. #htk_instructions()
#mydt=load("V:\\Nguyen_Ma\\BACKUP\\OECDtoolkit\\data\\resilience_database.Rda") data("htk_resilience") mydt=htk_resilience rm(htk_resilience) ##change to the path of your database ##please make sure that the format of the database is the same as the resilience_database example(ie, first column is countries' ISO3_code, second is the date, and each following column represents a variable) #mydt=rio::import("THE_LOCATION_OF_MY_DATASET")
icon_svg_path = "M19.404,6.65l-5.998-5.996c-0.292-0.292-0.765-0.292-1.056,0l-2.22,2.22l-8.311,8.313l-0.003,0.001v0.003l-0.161,0.161c-0.114,0.112-0.187,0.258-0.21,0.417l-1.059,7.051c-0.035,0.233,0.044,0.47,0.21,0.639c0.143,0.14,0.333,0.219,0.528,0.219c0.038,0,0.073-0.003,0.111-0.009l7.054-1.055c0.158-0.025,0.306-0.098,0.417-0.211l8.478-8.476l2.22-2.22C19.695,7.414,19.695,6.941,19.404,6.65z M8.341,16.656l-0.989-0.99l7.258-7.258l0.989,0.99L8.341,16.656z M2.332,15.919l0.411-2.748l4.143,4.143l-2.748,0.41L2.332,15.919z M13.554,7.351L6.296,14.61l-0.849-0.848l7.259-7.258l0.423,0.424L13.554,7.351zM10.658,4.457l0.992,0.99l-7.259,7.258L3.4,11.715L10.658,4.457z M16.656,8.342l-1.517-1.517V6.823h-0.003l-0.951-0.951l-2.471-2.471l1.164-1.164l4.942,4.94L16.656,8.342z" dl_button <- list( name = "Download data", icon = list( path = icon_svg_path, transform = "scale(0.84) translate(-1, -1)" ), click = htmlwidgets::JS(" function(gd) { var text = ''; for(var i = 0; i < gd.data.length; i++){ text += gd.layout.xaxis.title.text + gd.data[i].name + ',' + gd.data[i].x + '\\n'; text += gd.layout.yaxis.title.text + gd.data[i].name + ',' + gd.data[i].y + '\\n'; }; var blob = new Blob([text], {type: 'text/plain'}); var a = document.createElement('a'); const object_URL = URL.createObjectURL(blob); a.href = object_URL; a.download = 'data.csv'; document.body.appendChild(a); a.click(); URL.revokeObjectURL(object_URL); } ") )
Housing markets are large and experience has shown that both house price and construction cycles are subject to sharp swings. The functioning of housing markets strongly influence countries’ exposure to economic crises and their capacity to recover from them. This chapter analyses the role that housing-related policies play in (a) mitigating or amplifying shocks and (b) facilitating or hampering a recovery. It discusses how macroprudential measures, rental regulation and taxation can contribute to greater economic resilience.
My paragraph of description for the first policy outcome
My paragraph of description for the second policy outcome
My paragraph of description for the third policy outcome
My paragraph of description for the fourth policy outcome
My text of Executive summary of policies
Introduction or summary of the policy
My paragraph of policy recommendation
My paragraph of impact of policies
Paragraph1 of specific country policy
Paragraph2 of specific country policy
Introduction or summary of the policy
My paragraph of policy recommendation
My paragraph of impact of policies
Paragraph1 of specific country policy
Paragraph2 of specific country policy
- Author (date), "Title", OECD Economics Department Working Papers, No. 1555, OECD Publishing, Paris. https://oecdecoscope.blog/2019/07/18/are-there-ways-to-protect-economies-against-potential-future-housing-busts-2/
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