File:CzechResidentialRePrice.svg
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Captions
Captions
Czech residential real estate price
Summary[edit]
| Description |
English: Czech residential real estate price, plotted using Python code from Plotting code section. |
| Date | |
| Source | Own work |
| Author | Dan Polansky |
Licensing[edit]
I, the copyright holder of this work, hereby publish it under the following license:
This file is licensed under the Creative Commons Attribution 4.0 International license.
- You are free:
- to share – to copy, distribute and transmit the work
- to remix – to adapt the work
- Under the following conditions:
- attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
Plotting code[edit]
from matplotlib import pyplot as plt
import datetime
plotPrices = True
plot5YRates = True
# Data source: https://fred.stlouisfed.org/series/QCZN628BIS
# Residential Property Prices for Czech Republic, Index 2010=100, Quarterly, Not Seasonally Adjusted
inputDataFred = [
"2008-01-01:102.074",
"2008-04-01:106.2746",
"2008-07-01:107.8498",
"2008-10-01:107.2197",
"2009-01-01:104.9094",
"2009-04-01:101.549",
"2009-07-01:100.7088",
"2009-10-01:99.9737",
"2010-01-01:99.9737",
"2010-04-01:100.1838",
"2010-07-01:99.8687",
"2010-10-01:99.9737",
"2011-01-01:100.1838",
"2011-04-01:100.7088",
"2011-07-01:100.0788",
"2011-10-01:99.1336",
"2012-01-01:98.6086",
"2012-04-01:98.7136",
"2012-07-01:98.5035",
"2012-10-01:98.3985",
"2013-01-01:98.2935",
"2013-04-01:98.8186",
"2013-07-01:98.7136",
"2013-10-01:98.5035",
"2014-01-01:99.7637",
"2014-04-01:100.6038",
"2014-07-01:101.4439",
"2014-10-01:102.179",
"2015-01-01:103.2292",
"2015-04-01:104.3844",
"2015-07-01:105.6445",
"2015-10-01:106.7997",
"2016-01-01:108.1649",
"2016-04-01:110.3702",
"2016-07-01:113.1006",
"2016-10-01:118.4563",
"2017-01-01:122.0268",
"2017-04-01:125.0722",
"2017-07-01:127.2775",
"2017-10-01:128.4327",
"2018-01-01:131.268",
"2018-04-01:135.1536",
"2018-07-01:138.514",
"2018-10-01:141.1394",
"2019-01-01:144.0798",
"2019-04-01:147.6503",
"2019-07-01:150.5907",
"2019-10-01:153.7411",
"2020-01-01:156.4715",
"2020-04-01:159.0969",
"2020-07-01:163.1924",
"2020-10-01:167.498",
"2021-01-01:177.3694",
"2021-04-01:186.5056",
"2021-07-01:199.3174",
"2021-10-01:210.659",
"2022-01-01:220.8454",
"2022-04-01:228.0914",
"2022-07-01:230.4017",
"2022-10-01:225.256",
"2023-01-01:222.4206",
"2023-04-01:221.8955",
"2023-07-01:222.3156"]
# From https://vdb.czso.cz/
# For reference and is not used.
czsoData = {2006: 109, # 2005 index
2007: 128.9,
2008: 144.9,
2009: 138.1}
# Apartment prices, from https://www.cnb.cz/export/sites/cnb/cs/financni-stabilita/.galleries/zpravy_fs/fs_2010-2011/fs_2010-2011_kap_3_2.xls
# For reference only; not used
cnbApartmentData = [
"31.3.1999:100.0",
"30.6.1999:101.1",
"30.9.1999:102.3",
"31.12.1999:105.9",
"31.3.2000:110.4",
"30.6.2000:112.2",
"30.9.2000:110.8",
"31.12.2000:114.9",
"31.3.2001:118.9",
"30.6.2001:125.9",
"30.9.2001:129.9",
"31.12.2001:139.8",
"31.3.2002:148.6",
"30.6.2002:155.8",
"30.9.2002:157.5",
"31.12.2002:164.8",
"31.3.2003:177.5",
"30.6.2003:190.9",
"30.9.2003:192.7",
"31.12.2003:185.8",
"31.3.2004:184.3",
"30.6.2004:182.5",
"30.9.2004:180.7",
"31.12.2004:180.9",
"31.3.2005:181.8",
"30.6.2005:182.7",
"30.9.2005:183.6",
"31.12.2005:185.8",
"31.3.2006:189.8",
"30.6.2006:194.6",
"30.9.2006:198.7",
"31.12.2006:212.0",
"31.3.2007:237.9",
"30.6.2007:258.3",
"30.9.2007:268.7",
"31.12.2007:278.4",
"31.3.2008:292.4",
"30.6.2008:308.5",
"30.9.2008:313.7",
"31.12.2008:306.9",
"31.3.2009:288.5",
"30.6.2009:265.4",
"30.9.2009:260.5",
"31.12.2009:258.3",
"31.3.2010:257.3",
"30.6.2010:257.2",
"30.9.2010:255.6",
"31.12.2010:253.4",
"31.3.2011:248.9"]
# Real estate prices, from https://www.cnb.cz/export/sites/cnb/cs/financni-stabilita/.galleries/zpravy_fs/fs_2010-2011/fs_2010-2011_kap_3_2.xls
# Question: are commercial RE prices included? Nothing in the XLS says otherwise.
cnbRealEstateData = [
"31.3.1999:100.0",
"30.6.1999:103.0",
"30.9.1999:106.4",
"31.12.1999:110.8",
"31.3.2000:112.8",
"30.6.2000:115.4",
"30.9.2000:117.6",
"31.12.2000:120.8",
"31.3.2001:123.7",
"30.6.2001:127.8",
"30.9.2001:133.4",
"31.12.2001:139.9",
"31.3.2002:146.0",
"30.6.2002:149.5",
"30.9.2002:151.1",
"31.12.2002:153.7",
"31.3.2003:159.1",
"30.6.2003:165.4",
"30.9.2003:166.6",
"31.12.2003:163.8",
"31.3.2004:164.9",
"30.6.2004:165.6",
"30.9.2004:166.8",
"31.12.2004:167.0",
"31.3.2005:168.0",
"30.6.2005:170.1",
"30.9.2005:173.0",
"31.12.2005:176.8",
"31.3.2006:180.9",
"30.6.2006:185.0",
"30.9.2006:188.6",
"31.12.2006:195.3",
"31.3.2007:207.9",
"30.6.2007:219.1",
"30.9.2007:225.6",
"31.12.2007:231.1",
"31.3.2008:238.3",
"30.6.2008:248.3",
"30.9.2008:253.3",
"31.12.2008:251.7",
"31.3.2009:244.5",
"30.6.2009:234.9",
"30.9.2009:232.5",
"31.12.2009:231.6"]
dates = []
prices = []
calc5YRates = []
for dataRowStr in inputDataFred:
dataRow = dataRowStr.split(":")
date = datetime.datetime.strptime(dataRow[0], "%Y-%m-%d").date()
price = float(dataRow[1])
dates.append(date)
prices.append(price)
qInY = 4 # Quarter count in year
if len(prices) > 5 * qInY:
calc5YRates.append((float(prices[-1]) / prices[-1 - 5 * qInY]) ** (1/5.0))
else:
calc5YRates.append(float("nan"))
cnbIndexValues = []
cnbDates = []
for dataRowStr in cnbRealEstateData:
dataRow = dataRowStr.split(":")
date = datetime.datetime.strptime(dataRow[0], "%d.%m.%Y").date()
cnbIndexValue = float(dataRow[1]) * 0.4
cnbDates.append(date)
cnbIndexValues.append(cnbIndexValue)
datesUnited = sorted(set(dates) | set(cnbDates))
if plotPrices:
fig, biax = plt.subplots()
figSize = fig.get_size_inches()
fig.set_size_inches(figSize[0] * 4/3, figSize[1])
biax.set(xlabel="Date (label on 1 january)", ylabel="Index 1: 1999=40 points\nIndex 2: 2010=100 points", # 40 points match the 0.4 above.
title="Czech (residential) real estate prices, index, quarterly step (per CNB and stlouisfed.org)\n"+
"Series 1: RE prices from CNB; series 2: residential RE prices from stlouisfed.org\n" +
"Disclaimer: the visual juxtaposition of the two series may be misleading")
xtickDates, xtickDatesStr = [], []
for year in range(1999, 2023+1):
date = datetime.datetime.strptime(str(year) + "-01-01", "%Y-%m-%d").date()
xtickDates.append(date)
xtickDatesStr.append(str(year))
plt.ylim(0, 240)
plt.yticks([x * 20 for x in range(0, 13)])
plt.plot(dates, prices, color="#6699CC")
plt.plot(cnbDates, cnbIndexValues, color="#99CC66")
plt.xticks(xtickDates, xtickDatesStr, rotation=70)
plt.grid(linewidth=0.25, color="#CCCCCC")
plt.tight_layout()
plt.savefig("CzechResidentialRePrice.svg")
if plot5YRates: # Issue: there are not enough years for this to make sense
fig, biax = plt.subplots()
figSize = fig.get_size_inches()
fig.set_size_inches(figSize[0] * 4/3, figSize[1])
biax.set(xlabel="Withdrawal date (label on 1 January)", ylabel="Index change rate",
title="5-year change rate of Czech residential real estate, annualized (base data per stlouisfed.org)\n" +
"The date of withdrawal, the date of deposit being 5 years before")
xtickDates, xtickDatesStr = [], []
for idx, date in enumerate(dates):
if date.month == 1 and date.day == 1:
xtickDates.append(date)
xtickDatesStr.append(str(date.year))
plt.xticks(xtickDates, xtickDatesStr, rotation=70)
plt.axhline(1, color='#BBBBBB', linewidth=0.75)
plt.plot(dates, calc5YRates, color="#6699CC")
plt.grid(linewidth=0.25, color="#CCCCCC")
biax.set_yticklabels(['{:,.1%}'.format(x - 1) for x in biax.get_yticks()])
plt.tight_layout()
plt.savefig("CzechResidentialRePrice5YYield.svg")
|
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 09:59, 12 April 2024 | | 768 × 432 (79 KB) | Dan Polansky (talk | contribs) | Expand. |
| 14:54, 11 April 2024 |  | 768 × 432 (56 KB) | Dan Polansky (talk | contribs) | Uploaded own work with UploadWizard |
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