diff --git a/nb/fred-housing.ipynb b/nb/fred-housing.ipynb index 3ff98d7..cb2baf8 100644 --- a/nb/fred-housing.ipynb +++ b/nb/fred-housing.ipynb @@ -1,757 +1,854 @@ { - "metadata": { - "name": "", - "signature": "sha256:eac3814874891d68a53de448522181d53f218e59d6fc369bfd34fd00f48c0cb0" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Housing starts, home prices and affordibility" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Greenspan in 2014 pointed out that there was never a recovery from recession without improvements in housing construction. Here we examine some relevant data, including the Case-Shiller series, and derive an insightful measure of the housing economy which takes affordibility into account." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Dependencies:*\n", - "\n", - " - Linux, bash [not crucial, cross-platform prefered]\n", - " - Python: matplotlib, pandas [recommend Anaconda distribution]\n", - " - Modules: yi_1tools, yi_plot, yi_timeseries, yi_fred\n", - "\n", - "*CHANGE LOG*\n", - "\n", - " 2015-02-10 Code review and revision.\n", - " 2014-09-11 First version." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# NOTEBOOK settings and system details: [00-tpl v14.09.28]\n", - "\n", - "# Assume that the backend is LINUX (our particular distro is Ubuntu, running bash shell):\n", - "print '\\n :: TIMESTAMP of last notebook execution:'\n", - "!date\n", - "print '\\n :: IPython version:'\n", - "!ipython --version\n", - "\n", - "# Automatically reload modified modules:\n", - "%load_ext autoreload\n", - "%autoreload 2 \n", - "# 0 will disable autoreload.\n", - "# Generate plots inside notebook:\n", - "%matplotlib inline\n", - "\n", - "# DISPLAY options\n", - "from IPython.display import Image \n", - "# e.g. Image(filename='holt-winters-equations.png', embed=True)\n", - "from IPython.display import YouTubeVideo\n", - "# e.g. YouTubeVideo('1j_HxD4iLn8')\n", - "from IPython.display import HTML # useful for snippets\n", - "# e.g. HTML('')\n", - "import pandas as pd\n", - "print '\\n :: pandas version:'\n", - "print pd.__version__\n", - "# pandas DataFrames are represented as text by default; enable HTML representation:\n", - "# [Deprecated: pd.core.format.set_printoptions( notebook_repr_html=True ) ]\n", - "pd.set_option( 'display.notebook_repr_html', False )\n", - "\n", - "# MATH display, use %%latex, rather than the following:\n", - "# from IPython.display import Math\n", - "# from IPython.display import Latex\n", - "\n", - "print '\\n :: Working directory (set as $workd):'\n", - "workd, = !pwd\n", - "print workd + '\\n'" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " :: TIMESTAMP of last notebook execution:\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Wed Feb 11 16:44:59 PST 2015\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " :: IPython version:\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "2.3.0\r\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - " :: pandas version:\n", - "0.15.0\n", - "\n", - " :: Working directory (set as $workd):\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "/home/yaya/Dropbox/ipy/fecon235/nb\n", - "\n" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from yi_1tools import *\n", - "from yi_fred import *\n", - "from yi_plot import *\n", - "from yi_timeseries import *" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Housing Starts" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# in thousands of units\n", - "hs = getfred( m4housing )" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plotfred(hs)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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byBa9N6LbA+q554B//KPzcmcnKd3g+sWzz8axZ4/8V9dcA5xxhr2ua1c55ubN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\nEcYYeYPBYIgw/x80L908obfpcAAAAABJRU5ErkJggg==\n", - "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "What makes most sense over the long-term is the housing starts per capita." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# population in thousands\n", - "pop = getfred( m4pop )" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Factor 100.00 converts operation to float and percentage terms\n", - "hspop = todf((hs * 100.00) / pop)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plotfred(hspop)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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/hmHD8h8vE5li3vau4eKLC99felZMXQG/jPTPRiHeiUSCV17R1x066IUqXwbJ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\nskMFxsW8S4AVnHLUZQgLO1dlXV31hE0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- "text": [ - "" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So at the peaks, about 1% of the US population got allocated new housing monthly. The lowest point shown is after the Great Recession at 0.2%.\n", - "\n", - "Clearly there's a downward historical trend, so to discern short-term housing cycles, we detrend and normalize hspop." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plotfred( detrendnorm( hspop ) )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " :: regresstime slope = -0.000740096689954\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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NqQdGR4eMCblkicznsrSHDZNjqPI5Vzrr1snbh72h1+mhlk+0+/qABx+U7+3t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\nE5ftuedaDZL5KKtoKyEbOjS/aKsbZt0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- "text": [ - "" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Surprisingly, housing starts per capita during the Great Recession did not exceed two standard deviations on the downside. \n", - "\n", - "2015-02-10: It appears that housing starts has recovered relatively and is back to fairly average historical levels.\n", - "\n", - "In the concluding section, we shall derive another measure of housing activity which takes affordibility into account." - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Constructing a Home Price Index" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The correlation between Case-Shiller, 20-city vs 10-city, is practically 1. Thus a mash-up is warranted to get data extended back to 1987. Case-Shiller is not dollar denominated (but rather a chain of changes) so we use the median sales prices from 2000 to mid-2014 released by the National Association of Realtors to estimate home price, see function *gethomepx* for explicit details." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "homepx = getfred(m4homepx)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " :: Case-Shiller prepend successfully goes back to 1987.\n" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Case-Shiller is seasonally adjusted\n", - "plotfred(homepx)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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ksNOYbFLiNgVV/RBYG2udMaZ1AkapaoGqLgW+ApqLyBFANVWdFSw3HOgcPL8E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\n3aTFGGNM0bJ66OwmTZRJk9z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- "text": [ - "" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Geometric rate of return:\n", - "georet(homepx, 12)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 11, - "text": [ - "[3.62, 3.65, 2.55, 12]" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "As an asset class, residential homes are not a great investment." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Real home prices" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "defl = getfred(m4defl)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 12 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Real here means in current dollars:\n", - "homepxr = todf( homepx * defl )" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 13 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plotfred(homepxr)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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CzoZh5BCxWPJeRyVx9NEwYwZ8/z306gVDh2ZFmlEGbO4jwzDKzIIF0KkTrFjh4gNlYckS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\nSfv+9rdw3YwVMDu3YVQ9vvv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- "text": [ - "" - ] - } - ], - "prompt_number": 14 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Real geometric return:\n", - "georet(homepxr, 12)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 15, - "text": [ - "[1.21, 1.24, 2.57, 12]" - ] - } - ], - "prompt_number": 15 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the above does not account for annual property taxes which does eat up a large portion of real price appreciation.\n", - "\n", - "Perhaps home prices are only increasing because new stock of housing has been declining over the long-term.\n", - "\n", - "The years 1997-2006 is considered a **housing bubble** due to the widespread availability of *subprime mortgages* (cf. NINJA, No Income No Job Applicant, was often not rejected.) **Median home prices *doubled* in real terms**: from \\$140,000 to \\$280,000.\n", - "\n", - "**Great Recession took down home prices** (180-280)/280 = **-36% in real terms.**\n", - "\n", - "2015-02-10: we are roughly at 200/280 = 71% of peak home price in real terms. " - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Affordability for the typical home buyer" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For a sketch, we assume a fixed premium for 30-y mortgages over 10-y Treasuries. We then compute the number of hours needed to pay *only the interest on the full home price* (i.e. no down payment assumed).\n", - "\n", - "The sketch does not strive for exact precision, but serves as an indicator to model the housing economy." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "mortpremium = 1.50" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 16 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "mortgage = todf( getfred(m4bond10) + mortpremium )" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 17 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Yearly interest to be paid off:\n", - "interest = todf( homepx * (mortgage / 100.00) )" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 18 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Wage is in dollars per hour\n", - "wage = getfred(m4wage)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 19 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Working hours to pay off just the interest:\n", - "interesthours = todf( interest / wage )" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 20 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Mortgage interest to be paid as portion of annual income:\n", - "payhome = todf( interesthours / 2000.00 )\n", - "# We ignore tiny portion of mortgage payment made towards reducing principal.\n", - "plotfred( payhome )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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thZXIvvt65B1Vkp33qFEwYIDVyyml2vgeeReXCFbWDod86GXFirwnTKidgbBj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\nzdNpwM2a2RcNWBbG22+bs68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- "text": [ - "" - ] - } - ], - "prompt_number": 21 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we assume 2000 hours worked per year (40 hours for 50 weeks), we can see that interest payment can potentially take up to 50% of total annual pre-tax income. \n", - "\n", - "2015-02-10: Currently that figure is about 20% so housing should be affordable, but the population is uncertain about the risk on taking on debt. (What if I become unemployed?) \n", - "\n", - "Prospects of deflation adds to the fear of such risk. Debt is best taken on in inflationary environments." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Forecast payhome for the next 12 months:\n", - "holtfred( payhome, 12 )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 22, - "text": [ - " Forecast\n", - "0 0.190100\n", - "1 0.194167\n", - "2 0.191988\n", - "3 0.189809\n", - "4 0.187630\n", - "5 0.185451\n", - "6 0.183272\n", - "7 0.181093\n", - "8 0.178914\n", - "9 0.176735\n", - "10 0.174556\n", - "11 0.172376\n", - "12 0.170197" - ] - } - ], - "prompt_number": 22 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "2015-02-10: Homes should be slightly more affordable: 17% of annual income -- perhaps due to further declining interest rates, or even some increase in wages for the typical American worker.\n", - "\n", - "Caution: although the numbers may indicate increased affordability, it has become far more difficult to obtain mortgage financing due to strict credit requirements. The pendulum of scrutiny from the NINJA days of the subprime era has swung to the opposite extreme. Subprime mortgages were the root cause of the Great Recession." - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Housing starts scored by affordability" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The basic idea is that housing starts should be more abundant when homes are more affordable. Recall that the proxy for afforability is our variable *payhome* which was constructed as a function of home price, interest rate, and wage income. " + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Housing economy, home prices and affordibility\n", + "\n", + "Alan Greenspan in 2014 pointed out that there was never a recovery from recession \n", + "without improvements in housing construction. Here we examine some relevant data, \n", + "including the Case-Shiller series, and derive an insightful \n", + "measure of the housing economy, **hscore**, which takes affordibility into account.\n", + "\n", + "Contents:\n", + "\n", + "- Housing Starts\n", + "- Constructing a Home Price Index\n", + "- Real home prices\n", + "- Indebtedness for typical home buyer\n", + "- hscore: Housing starts scored by affordability\n", + "- Concluding remarks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Dependencies:*\n", + "\n", + "- Repository: https://github.com/rsvp/fecon235\n", + "- Python: matplotlib, pandas\n", + "\n", + "*CHANGE LOG*\n", + "\n", + " 2016-02-08 Fix issue #2 by v4 and p6 updates.\n", + " Our hscore index has been completely revised.\n", + " Another 12 months of additional data.\n", + " 2015-02-10 Code review and revision.\n", + " 2014-09-11 First version." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from fecon235.fecon235 import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " :: Python 2.7.11\n", + " :: IPython 4.0.0\n", + " :: jupyter 1.0.0\n", + " :: notebook 4.0.6\n", + " :: matplotlib 1.4.3\n", + " :: numpy 1.10.1\n", + " :: pandas 0.17.1\n", + " :: pandas_datareader 0.2.0\n", + " :: Repository: fecon235 v4.16.0123 develop\n", + " :: Timestamp: 2016-02-10, 20:12:07 UTC\n", + " :: $pwd: /media/yaya/virt15h/virt/dbx/Dropbox/ipy/fecon235/nb\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# hspop can be interpreted as the percentage of the population allocated new housing.\n", - "# Let's weight hspop by payhome to score housing starts:\n", - "hscore = todf( hspop * payhome )" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 23 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plotfred( hscore )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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szsNTiLWMqakxK6alX38X8cKegbFj4a23YpmSx1Nqkgl7OisGrDFAdXVx4unU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\ngA+AR1R1rohcISJXAIhILxFZBlwLfE9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- "text": [ - "" - ] - } - ], - "prompt_number": 24 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The mean of *hscore* is approximately 0.17, and we observe a stable band between 0.15 and 0.20 from 1991 to 2004. That band can be interpreted as an equilibrium region for the housing economy (before the Housing Bubble and Great Recession). It's also worth noting that long-term interest rates during that epoch was determined by the market -- yet untouched by the massive *quantitative easing* programs initiated by the Federal Reserve." + } + ], + "source": [ + "# PREAMBLE-p6.15.1223 :: Settings and system details\n", + "from __future__ import absolute_import, print_function\n", + "system.specs()\n", + "pwd = system.getpwd() # present working directory as variable.\n", + "print(\" :: $pwd:\", pwd)\n", + "# If a module is modified, automatically reload it:\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "# Use 0 to disable this feature.\n", + "\n", + "# Notebook DISPLAY options:\n", + "# Represent pandas DataFrames as text; not HTML representation:\n", + "import pandas as pd\n", + "pd.set_option( 'display.notebook_repr_html', False )\n", + "# Beware, for MATH display, use %%latex, NOT the following:\n", + "# from IPython.display import Math\n", + "# from IPython.display import Latex\n", + "from IPython.display import HTML # useful for snippets\n", + "# e.g. HTML('')\n", + "from IPython.display import Image \n", + "# e.g. Image(filename='holt-winters-equations.png', embed=True) # url= also works\n", + "from IPython.display import YouTubeVideo\n", + "# e.g. YouTubeVideo('1j_HxD4iLn8', start='43', width=600, height=400)\n", + "from IPython.core import page\n", + "get_ipython().set_hook('show_in_pager', page.as_hook(page.display_page), 0)\n", + "# Or equivalently in config file: \"InteractiveShell.display_page = True\", \n", + "# which will display results in secondary notebook pager frame in a cell.\n", + "\n", + "# Generate PLOTS inside notebook, \"inline\" generates static png:\n", + "%matplotlib inline \n", + "# \"notebook\" argument allows interactive zoom and resize." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Housing Starts\n", + "\n", + "*Housing starts* is an economic indicator that reflects the number of \n", + "privately owned new houses (technically housing units) on which \n", + "construction has been started in a given period. \n", + "We retrieve monthly data released by the U.S. Bureau of the Census. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# In thousands of units:\n", + "hs = get( m4housing )\n", + "# m4 indicates monthly frequency." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# plot( hs )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since housing is what houses people, over the long-term \n", + "it is reasonable to examine **housing starts per capita**." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# US population in thousands:\n", + "pop = get( m4pop )" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Factor 100.00 converts operation to float and percentage terms:\n", + "hspop = todf((hs * 100.00) / pop)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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TTbx//nMd03KvveD99z3xfu892Hnn5HXDiLe9hfc3xz/ySH23/uoFF8Af/qD7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\njQLLJd6F4hfvqVNLG6FZi2bLlvSRd/C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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot( hspop )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**At the peaks, about 1% of the *US population got allocated new housing monthly*.\n", + "The lowest point shown is after the Great Recession at 0.2%.**\n", + "\n", + "Clearly there's a downward historical trend, so to discern **short-term housing cycles**,\n", + "we detrend and normalize hspop." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " :: regresstime slope = -0.000742599274435\n" ] }, { - "cell_type": "heading", - "level": 2, + "data": { + "image/png": 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b7XiE6ZL0u/GRqUiP7duBxx7LL0VlJtG2HkdzlMn558vQZPX1ud006eqq/M7W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\nPiQqKmQ0ITc+7UIT7YsvlhszHS0twOT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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "Housing score normalized =: hscoren" + "output_type": "display_data" + } + ], + "source": [ + "plot(detrendnorm( hspop ))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Surprisingly, housing starts per capita during the Great Recession did not\n", + "exceed two standard deviations on the downside. \n", + "\n", + "2015-02-10 and 2016-02-08: It appears that housing starts has recovered relatively\n", + "and is back to mean trend levels.\n", + "\n", + "In the concluding section, we shall derive another measure of housing activity\n", + "which takes affordibility into account." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing a Home Price Index\n", + "\n", + "The correlation between Case-Shiller indexes, 20-city vs 10-city, is practically 1.\n", + "Thus a mash-up is warranted to get data extended back to 1987.\n", + "Case-Shiller is not dollar denominated (but rather a chain of changes)\n", + "so we use the median sales prices from 2000 to mid-2014 released by the\n", + "National Association of Realtors to estimate home price,\n", + "see function **gethomepx** for explicit details." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[1;31mSignature: \u001b[0m\u001b[0mgethomepx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfredcode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'm4homepx'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mSource:\u001b[0m\n", + "def gethomepx( fredcode=m4homepx ):\n", + " '''Make Case-Shiller 20-city, and try to prepend 1987-2000 10-city.'''\n", + " # Fred's licensing may change since source is S&P, \n", + " # however, we have a local copy of 1987-2013 monthly SA data.\n", + " hpnow = getdata_fred( 'SPCS20RSA' )\n", + " # 20-city home price index back to 2000-01-01.\n", + " try:\n", + " hpold = readfile( 'FRED-home-Case-Shiller_1987-2013.csv.gz', compress='gzip' )\n", + " # ^includes 10-city index from 1987-2000.\n", + " # Current correlation with 20-city: 0.998\n", + " # Thus the mashup is justified.\n", + " hpall = hpold.combine_first( hpnow )\n", + " # ^appends dataframe\n", + " print(' :: Case-Shiller prepend successfully goes back to 1987.')\n", + " except:\n", + " hpall = hpnow\n", + " print(' :: Case-Shiller since 2000 (1987-archive not found).')\n", + " # Case-Shiller is not dollar based, so we use:\n", + " # Median Sales Price of Existing Homes\n", + " # from the National Association of Realtors, fredcode: HOSMEDUSM052N\n", + " dollarindex = 183700.57 / 153.843\n", + " # means: ^Realtor$ ^C-S 20-city from 2000-01-01 to 2014-06-01.\n", + " return hpall * dollarindex\n", + "\u001b[1;31mFile: \u001b[0m~/Dropbox/ipy/fecon235/lib/yi_fred.py\n", + "\u001b[1;31mType: \u001b[0mfunction" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# We can use ? or ?? to extract code info:\n", + "gethomepx??" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " :: Case-Shiller prepend successfully goes back to 1987.\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Let's normalize to dimensionless unit:\n", - "hscoren = normalize( hscore )\n", - "plotfred( hscoren )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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fP5xxT5OmTeEb34BXXw1aiWNv5L33LMURzLh7nnuyTJlCc/TRMH68PS8tdXVm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\nj7mLyDdFZIaI7BaRw1K0Gykis0Vknoi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- "text": [ - "" - ] - } - ], - "prompt_number": 25 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "During the Housing Bubble our hscore reaches $ +2.0 \\sigma $.\n", - "\n", - "Great Recession: There is evidence recently that families shifted to home rentals, avoiding home ownership which would entail taking on mortgage debt. Some home owners experienced negative equity. And when the debt could not be paid due to wage loss, it seemed reasonable to walk away from their homes, even if that meant damage to their credit worthiness. Housing construction had to compete with a large supply of foreclosed homes on the market.\n", - "\n", - "2015-02-11: hscore is currently $ -1.29 \\sigma $ which suggests that **housing starts weighted by affordability has still not truly recovered from the Great Recession**.\n" + } + ], + "source": [ + "# Our interface will not ask the user to enter such messy details...\n", + "homepx = get( m4homepx )\n", + "# m4 indicates monthly home prices." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Case-Shiller is seasonally adjusted:\n", + "plot( homepx )\n", + "# so the plot appears relatively smooth. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[3.7, 3.73, 2.52, 12, 347, '1987-01-01', '2015-11-01']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Geometric rate of return since 1987:\n", + "georet( homepx, 12 )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first element tells us home prices have increased\n", + "approximately 3.7% per annum.\n", + "The third element shows price volatility of 2.5%\n", + "which is very low compared to other asset classes.\n", + "\n", + "But this does not take into account inflation.\n", + "In any case, recent home prices are still below\n", + "the levels just before the Great Recession." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Real home prices" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# This is an synthetic deflator created from four sources:\n", + "# CPI and PCE, both headline and core:\n", + "defl = get( m4defl )" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# \"Real\" will mean in terms of current dollars:\n", + "homepxr = todf( homepx * defl )\n", + "# r for real " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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FZPdKZtRLV0BE6gPPAI+p6vOB9BLgNOCkQPHlwL6B431wv/CX+/3k9MQ5+wGf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\n61S1wO8LsE5VG4vIAABVvd3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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot( homepxr )" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[1.32, 1.35, 2.54, 12, 347, '1987-01-01', '2015-11-01']" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Real geometric return of home prices:\n", + "georet( homepxr, 12 )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Real* home prices since 1987 have increased at the approximate\n", + "rate of +1.3% per annum.\n", + "\n", + "Note that the above does not account for annual property taxes\n", + "which could diminish of real price appreciation.\n", + "\n", + "Perhaps home prices are only increasing because new stock of housing\n", + "has been declining over the long-term (as shown previously).\n", + "\n", + "The years 1997-2006 is considered a **housing bubble**\n", + "due to the widespread availability of *subprime mortgages*\n", + "(cf. NINJA, No Income No Job Applicant, was often not rejected.)\n", + "**Median home prices *doubled* in real terms**: from \\$140,000 to \\$280,000.\n", + "\n", + "**Great Recession took down home prices** (180-280)/280 = **-36% in real terms.**\n", + "\n", + "2015-02-10: we are roughly at 200/280 = 71% of peak home price in real terms.\n", + "\n", + "2016-02-08: we are roughly at 220/280 = 79% of peak home price in real terms." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Indebtedness for typical home buyer\n", + "\n", + "For a sketch, we assume a fixed premium for some long-term mortgages over 10-y Treasuries,\n", + "and then compute the number of hours needed to \n", + "pay *only the interest on the full home price* (i.e. no down payment assumed).\n", + "\n", + "This sketch does not strive for strict veracity, but simply serves as an\n", + "indicator to model the housing economy." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mortpremium = 1.50" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mortgage = todf( get(m4bond10) + mortpremium )" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Yearly interest to be paid off:\n", + "interest = todf( homepx * (mortgage / 100.00) )" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Wage is in dollars per hour:\n", + "wage = get( m4wage )" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Working hours to pay off just the interest:\n", + "interesthours = todf( interest / wage )" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Mortgage interest to be paid as portion of ANNUAL income,\n", + "# assuming 2000 working hours per year:\n", + "payhome = todf( interesthours / 2000.00 )\n", + "\n", + "# We ignore tiny portion of mortgage payment made towards reducing principal.\n", + "# And of course, the huge disparity in earned income among the population." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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fK1bk9zyffFK1s7ROHaioSFwnw+Ef1qyxTJNYfvc7eOopeyj7Bdfy9hclGbwL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\n+ftvwN9yL6106d7dRl3u2mV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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot( payhome )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we assume 2000 hours worked per year (40 hours for 50 weeks), we can see that\n", + "interest payment can potentially take up to 50% of total annual pre-tax income. \n", + "\n", + "2015-02-10: Currently that figure is about 20% so housing should be affordable,\n", + "but the population is uncertain about the risk on taking on debt.\n", + "(What if unemployment looms in the future?) \n", + "\n", + "Prospects of deflation adds to the fear of such risk.\n", + "Debt is best taken on in inflationary environments.\n", + "\n", + "The housing bubble clearly illustrated that\n", + "huge *price risk* of the underlying asset could be an important consideration.\n", + "\n", + "Thus the renting a home (without any equity stake) may appear preferable over buying a home." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# # Forecast payhome for the next 12 months:\n", + "# forecast( payhome, 12 )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2016-02-09: Homes should be slightly more affordable: 19% of annual income -- perhaps\n", + "due to further declining interest rates, or even some\n", + "increase in wages for the typical American worker.\n", + "\n", + "Caution: although the numbers may indicate increased affordability,\n", + "it has become *far more difficult to obtain mortgage financing due to\n", + "strict credit requirements*. The pendulum of scrutiny from the NINJA days of the\n", + "subprime era has swung to the opposite extreme.\n", + "Subprime mortgages were the root cause of the Great Recession.\n", + "This would require another notebook which studies credit flows\n", + "from financial institutions to home buyers.\n", + "\n", + "Great Recession: There is evidence recently that families shifted to home rentals,\n", + "avoiding home ownership which would entail taking on mortgage debt.\n", + "Some home owners experienced negative equity.\n", + "And when the debt could not be paid due to wage loss, it seemed reasonable to\n", + "walk away from their homes, even if that meant damage to their credit worthiness.\n", + "*Housing construction had to compete with a large supply of foreclosed homes on the market.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## hscore: Housing starts scored by affordability\n", + "\n", + "The basic idea here is that housing starts can be weighted by some\n", + "proxy of \"affordability.\"\n", + "An unsold housing unit cannot be good for a healthy economy.\n", + "\n", + "Recall that our variable *payhome* was constructed as a function of\n", + "home price, interest rate, and wage income -- to solve for the portion\n", + "of annual income needed to pay off a home purchase -- i.e. indebtedness.\n", + "\n", + "**Home affordability** can thus be *abstractly* represented as 0 < (1-payhome) < 1,\n", + "by ignoring living expenses of the home buyer." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "afford = todf( 1 - payhome )" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# hspop can be interpreted as the percentage of the population allocated new housing.\n", + "\n", + "# Let's weight hspop by afford to score housing starts...\n", + "hscore = todf( hspop * afford )\n", + "\n", + "# ... loosely interpretated as new \"affordable\" housing relative to population." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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NzO1kyuDdXfmnXcuEzWz9dOea+60YUGFvbtbxUqqqtBQy27vAnTthyRItWSwu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\n/7yMGxdh40Z4883Cv/9ky+eeG6GoyFveuzfCuHH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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot( hscore )" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Y\n", + "count 347.000000\n", + "mean 0.313257\n", + "std 0.091287\n", + "min 0.124288\n", + "25% 0.244456\n", + "50% 0.316766\n", + "75% 0.387574\n", + "max 0.483924\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Forecast for hscoren, 12-months ahead:\n", - "holtfred( hscoren, 12 )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 26, - "text": [ - " Forecast\n", - "0 -1.287390\n", - "1 -1.261343\n", - "2 -1.262910\n", - "3 -1.264478\n", - "4 -1.266046\n", - "5 -1.267613\n", - "6 -1.269181\n", - "7 -1.270749\n", - "8 -1.272316\n", - "9 -1.273884\n", - "10 -1.275451\n", - "11 -1.277019\n", - "12 -1.278587" - ] - } - ], - "prompt_number": 26 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 26 } ], - "metadata": {} + "source": [ + "stat( hscore )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**hscore** can be roughly interpreted as \"affordable\" housing starts\n", + "expressed as percentage of the total U.S. population.\n", + "\n", + "The overall mean of *hscore* is approximately 0.31, and we observe a band between\n", + "0.31 and 0.47 from 1993 to 2004.\n", + "That band could be interpreted as an equilibrium region for the housing economy\n", + "(before the Housing Bubble and Great Recession).\n", + "It's also worth noting that long-term interest rates during that epoch was\n", + "determined by the market -- yet untouched by the massive *quantitative easing*\n", + "programs initiated by the Federal Reserve." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + " Forecast\n", + "0 0.295048\n", + "1 0.297774\n", + "2 0.300460\n", + "3 0.303146\n", + "4 0.305832\n", + "5 0.308518\n", + "6 0.311204\n", + "7 0.313890\n", + "8 0.316576\n", + "9 0.319262\n", + "10 0.321948\n", + "11 0.324634\n", + "12 0.327320" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Forecast for hscore, 12-months ahead:\n", + "forecast( hscore, 12 )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Concluding remarks\n", + "\n", + "We created an index **hscore** which expresses new \"affordable\" housing units\n", + "as percentage of total population. Affordability was crudely modeled by a few\n", + "well-known economic variables, plus our extended Case-Schiller index\n", + "of median home prices.\n", + "\n", + "- 2016-02-09 Following the Great-Recession lows around 0.13, *hscore* has now reverted to its long-term mean of 0.31, *confirming the recovery*, and is forecasted to slightly increase to 0.33.\n", + "\n", + "\n", + "- The Fed terminated its QE program but has not sold off any of its mortgage securities. That reduces upward pressure on mortgage rates. However, our *hscore* supports the Fed's rate hike decision on 2015-12-16 since it gives evidence that the housing market has recovered midway between the housing bubble and the subprime mortgage crisis." + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}