21:null 22:["$","title",null,{"children":"Assignment 2: Manufactured Homes in the United States | Anna Elizabeth Morgan | Observable"}] 27:T694,{ const data = averagePriceByYear.filter( d => d.Year >= 2020 && d.Year <= 2026 ); const colorScale = { domain: ['Average Price', 'Average Size'], range: ['#0072B2', '#FF0000'] }; const legend = { title: null, symbolSize: 200, labelFontSize: 12 }; const priceline = vl.markLine({opacity: 1, point: true}) .data(data) .encode( vl.x().fieldO('Year') .axis({ title: 'Year', titleFontSize: 16, labelFontSize: 12 }), vl.y().fieldQ('AveragePrice') .axis({ orient: 'left', title: 'Average Price ($)', titleColor: '#0072B2', titleFontSize: 16, labelFontSize: 12 }), vl.color().datum('Average Price').type('nominal') .scale(colorScale) .legend(legend) ); const sqftline = vl.markLine({opacity: 1, point: true}) .data(data) .encode( vl.x().fieldO('Year') .axis({ title: 'Year', titleFontSize: 16, labelFontSize: 12 }), vl.y().fieldQ('AverageSQFT') .scale({domain: [0, 1700], nice: false}) .axis({ orient: 'right', title: 'Average Size (sqft)', titleColor: '#FF0000', titleFontSize: 16, labelFontSize: 12, grid: false }), vl.color().datum('Average Size').type('nominal') .scale(colorScale) .legend(legend) ); return vl.layer(priceline, sqftline) .resolve({scale: {y: 'independent'}}) .title({ text: 'Average Price and Size', fontSize: 20 }) .width(400) .height(300) .render(); }28:T6fc,{ const datasets = [ [2020, data2020], [2022, data2022], [2024, data2024] ]; const prepared = datasets.map(([year, rows]) => ({ year, data: rows .map(row => ({ Bedrooms: numberValue(row.BEDROOMS), SQFT: numberValue(row.SQFT) })) .filter(d => [1, 3].includes(d.Bedrooms) && Number.isFinite(d.SQFT) && d.SQFT > 0 ) .map(d => ({ ...d, BedroomGroup: d.Bedrooms === 1 ? '≤2 bedrooms' : '>2 bedrooms' })) })); const binWidth = 200; const maxSQFT = Math.ceil( prepared.reduce( (max, dataset) => dataset.data.reduce( (m, d) => Math.max(m, d.SQFT), max ), binWidth ) / binWidth ) * binWidth; const charts = prepared.map(({year, data}) => vl.markLine({opacity: 1}) .data(data) .encode( vl.x().fieldQ('SQFT') .bin({step: binWidth, extent: [0, maxSQFT]}) .axis({ title: 'Home Area (sq ft)', titleFontSize: 14, labelFontSize: 12 }), vl.y().count() .stack(null) .axis({ title: 'Number of Records', titleFontSize: 14, labelFontSize: 12 }), vl.color().fieldN('BedroomGroup') .scale({ domain: ['≤2 bedrooms', '>2 bedrooms'], range: ['#0072B2', '#E69F00'] }) .legend({ title: 'Bedrooms', labelFontSize: 12, titleFontSize: 14 }) ) .title({text: String(year), fontSize: 18}) .width(250) .height(300) ); return vl.hconcat(...charts) .resolve({ scale: {x: 'shared', y: 'shared', color: 'shared'} }) .render(); }29:T529,{ const data = regionalAveragePrices.filter( d => d.Year >= 2020 && d.Year <= 2026 ); const line = vl.markLine({opacity: 1}) .data(data) .encode( vl.x().fieldO('Year').scale({rangeStep: 1}) .axis({title: 'Year', titleFontSize: 16, labelFontSize: 12}), vl.y().fieldQ('AveragePrice') .axis({ title: 'Average Price ($)', titleFontSize: 16, labelFontSize: 12 }), vl.color().fieldN('Region') .scale({range: ['#0072B2', '#E69F00', '#009E73', '#FF0000']}) .legend({ symbolSize: 100, labelFontSize: 12, titleFontSize: 14 }) ); const dot = vl.markCircle({opacity: 1, size: 60}) .data(data) .encode( vl.x().fieldO('Year').scale({rangeStep: 1}) .axis({title: 'Year', titleFontSize: 16, labelFontSize: 12}), vl.y().fieldQ('AveragePrice') .axis({ title: 'Average Price ($)', titleFontSize: 16, labelFontSize: 12 }), vl.color().fieldN('Region').legend({ symbolSize: 100, labelFontSize: 12, titleFontSize: 14 }) ); return vl.layer(line, dot) .title({ text: "Average Price Over Time by Region", fontSize: 20 }) .width(400) .height(300) .render(); }2a:T4ea,{ const stackOrder = { 'Personal property': 0, 'Real estate': 1, 'Not titled': 2 }; const data = titleProportionsByYear .filter(d => d.Year >= 2020 && d.Year <= 2026) .map(d => ({ ...d, StackOrder: stackOrder[d.Category] })); return vl.markBar({opacity: 1}) .data(data) .encode( vl.x().fieldO('Year') .axis({ title: 'Year', labelAngle: 0, titleFontSize: 16, labelFontSize: 12 }), vl.y().fieldQ('Proportion') .stack('zero') .scale({domain: [0, 1]}) .axis({ title: 'Proportion', format: '.0%', titleFontSize: 16, labelFontSize: 12 }), vl.order().fieldQ('StackOrder').sort('ascending'), vl.color().fieldN('Category') .scale({ domain: ['Not titled', 'Personal property', 'Real estate'], range: ['#0072B2', '#E69F00', '#009E73'] }) .legend({ title: 'Title Category', symbolSize: 100, labelFontSize: 12, titleFontSize: 14 }) ) .title({ text: 'Home Titling Trends Over Time', fontSize: 20 }) .width(800) .height(300) .render(); }2b:T836,{ const months = [ 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec' ]; const data = statusProportionDisplay .filter(d => d.Year >= 2020 && d.Year <= 2026) .map(d => { const value = String(d.Proportion).trim(); return { ...d, Year: Number(d.Year), MonthNumber: Number( String(d.ShipmentMonth ?? d.Month).trim().slice(-2) ), Proportion: value.endsWith('%') ? Number(value.slice(0, -1)) / 100 : Number(value) }; }); return vl.markArea({opacity: 0.8}) .data(data) .encode( vl.row().fieldO('Year') .sort('ascending') .header({ title: null, labelAngle: 0, labelFontSize: 14, format: 'd' }), vl.x().fieldQ('MonthNumber') .scale({domain: [1, 12], nice: false}) .axis({ title: 'Month Shipped', values: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], labelExpr: `${JSON.stringify(months)}[datum.value - 1]`, labelAngle: 0, grid: true, titleFontSize: 16, labelFontSize: 12 }), vl.y().fieldQ('Proportion') .stack(null) .scale({domain: [0, 1], nice: false}) //.axis(null), .axis({title: null}), vl.color().fieldN('Year') .scale({ domain: [2020, 2021, 2022, 2023, 2024, 2025], range: [ '#0072B2', '#E69F00', '#009E73', '#FF0000', '#BB00FF', '#000000' ] }) .legend(null), vl.tooltip([ {field: 'Year', type: 'ordinal', title: 'Year'}, {field: 'MonthNumber', type: 'ordinal', title: 'Month'}, { field: 'Proportion', type: 'quantitative', title: 'Sold/Leased within 4 Months', format: '.1%' } ]) ) .title({ text: 'Proportion Sold in 4 Months by Shipment Month', fontSize: 20 }) .width(400) .height(65) .config({view: {stroke: null}}) .render(); }2c:T954,{ const statusNames = { 1: 'Sold/Leased', 2: 'Not Yet Sold/Leased', 3: 'N/A' }; const rows = data2025 .map(row => ({ Footing: numberValue(row.FOOTINGS ?? row.footings), StatusCode: statusValue(row), Weight: weightValue(row) * wgtAdjValue(row) })) .filter(d => [1,2,3,4,5].includes(d.Footing) && Number.isFinite(d.Footing) && [1, 2].includes(d.StatusCode) && Number.isFinite(d.Weight) && d.Weight > 0 ); const footingTotals = new Map(); const categoryTotals = new Map(); for (const row of rows) { footingTotals.set( row.Footing, (footingTotals.get(row.Footing) ?? 0) + row.Weight ); const key = `${row.Footing}|${row.StatusCode}`; categoryTotals.set( key, (categoryTotals.get(key) ?? 0) + row.Weight ); } const data = Array.from(categoryTotals, ([key, weight]) => { const [footing, statusCode] = key.split("|").map(Number); return { Footing: footing, StatusCode: statusCode, Status: statusNames[statusCode], Proportion: weight / footingTotals.get(footing) }; }); return vl.markBar({opacity: 1}) .data(data) .encode( vl.x().fieldO('Footing') .sort('ascending') .axis({ title: 'Footing Type', labelExpr: `{ '1': 'Monolithic-slab Foundation', '2': 'Basement/Crawl Space', '3': 'Concrete Footings', '4': 'Pressure Treated Wood', '5': 'Other' }[datum.value]`, labelAngle: -35, labelLimit: 300, titleFontSize: 16, labelFontSize: 12 }), vl.y().fieldQ('Proportion') .stack('zero') .scale({domain: [0, 1]}) .axis({ title: 'Proportion of Units', format: '.0%', titleFontSize: 16, labelFontSize: 12 }), vl.color().fieldN('Status') .scale({ domain: [statusNames[1], statusNames[2]], range: ['#0072B2', '#E69F00'] }) .legend({ title: 'Status', labelFontSize: 12, titleFontSize: 14 }), vl.order().fieldQ('StatusCode').sort('ascending') ) .title({ text: 'Sold Status of Homes With Different Footings After 4 Months — 2025', fontSize: 20 }) .width(600) .height(300) .render(); }2d:T94e,{ const footingLabels = { 1: 'Monolithic-slab foundation', 2: 'Basement/crawl space', 3: 'Concrete footings', 4: 'Pressure treated wood or ABS footings', 5: 'Other' }; const rows = data2025 .map(row => ({ Footing: numberValue(row.FOOTINGS ?? row.footings), RegionCode: numberValue(row.REGION ?? row.region), Weight: weightValue(row) * wgtAdjValue(row) })) .filter(d => [1, 2, 3, 4, 5].includes(d.Footing) && [1, 2, 3, 4].includes(d.RegionCode) && Number.isFinite(d.Weight) && d.Weight > 0 ); const regionTotals = new Map(); const categoryTotals = new Map(); for (const row of rows) { regionTotals.set( row.RegionCode, (regionTotals.get(row.RegionCode) ?? 0) + row.Weight ); const key = `${row.RegionCode}|${row.Footing}`; categoryTotals.set( key, (categoryTotals.get(key) ?? 0) + row.Weight ); } const data = Array.from(categoryTotals, ([key, weight]) => { const [regionCode, footing] = key.split("|").map(Number); return { Region: regionNames.get(regionCode), Footing: footing, FootingType: footingLabels[footing], Proportion: weight / regionTotals.get(regionCode) }; }); return vl.markBar({opacity: 1}) .data(data) .encode( vl.x().fieldN('Region') .sort(['Northeast', 'Midwest', 'South', 'West']) .axis({ title: 'Region', labelAngle: 0, titleFontSize: 16, labelFontSize: 12 }), vl.y().fieldQ('Proportion') .stack('zero') .scale({domain: [0, 1]}) .axis({ title: 'Proportion of Homes', format: '.0%', titleFontSize: 16, labelFontSize: 12 }), vl.color().fieldN('FootingType') .scale({ domain: [1, 2, 3, 4, 5].map(code => footingLabels[code]), range: [ '#0072B2', '#FF0000', '#009E73', '#FFA500', '#777777' ] }) .legend({ title: 'Footing Type', labelLimit: 350, labelFontSize: 12, titleFontSize: 14 }), vl.order().fieldQ('Footing').sort('ascending') ) .title({ text: 'Footing Types Within Each Region — 2025', fontSize: 20 }) .width(600) .height(300) .render(); }2e:T630,import * as XLSX from "npm:xlsx"; async function readFirstXlsxSheet(file) { const workbook = await file.xlsx(); const sheetName = workbook.sheetNames[0]; return workbook.sheet(sheetName, { headers: true }); } async function readFirstLegacyXlsSheet(file) { const buffer = await file.arrayBuffer(); const workbook = XLSX.read(buffer, {type: "array"}); const sheetName = workbook.SheetNames[0]; return XLSX.utils.sheet_to_json( workbook.Sheets[sheetName], {defval: null} ); } const [ data2014, data2015, data2016, data2017, data2018, data2020, data2021, data2022, data2023, data2024, data2025 ] = await Promise.all([ readFirstXlsxSheet(FileAttachment("PUF2014.xlsx")), readFirstXlsxSheet(FileAttachment("PUF2015.xlsx")), readFirstXlsxSheet(FileAttachment("puf2016.xlsx")), readFirstXlsxSheet(FileAttachment("PUF2017.xlsx")), readFirstXlsxSheet(FileAttachment("PUF2018.xlsx")), readFirstXlsxSheet(FileAttachment("PUF2020final_v1coll.xlsx")), readFirstLegacyXlsSheet(FileAttachment("PUF2021final_v1.xls")), readFirstLegacyXlsSheet(FileAttachment("puf2022.xls")), readFirstXlsxSheet(FileAttachment("PUF2023.xlsx")), readFirstXlsxSheet(FileAttachment("PUF2024.xlsx")), readFirstXlsxSheet(FileAttachment("PUF2025.xlsx")) ]); display({ 2014: data2014.length, 2015: data2015.length, 2016: data2016.length, 2017: data2017.length, 2018: data2018.length, 2020: data2020.length, 2021: data2021.length, 2022: data2022.length, 2023: data2023.length, 2024: data2024.length, 2025: data2025.length });2f:T503,statusProportionDisplay = statusMonthKeys.map(key => { const [year, shipmentMonth] = key.split("|").map(Number); const sourceRows = datasetsByYear.find( ([datasetYear]) => datasetYear === year )[1]; const selectedStatus = year <= 2020 ? 2 : 1; const monthlyRows = sourceRows.filter(row => { const weight = weightValue(row) * wgtAdjValue(row); return ( shipmentMonthValue(row) === shipmentMonth && [1, 2, 3].includes(statusValue(row)) && Number.isFinite(weight) && weight > 0 ); }); const totalWeight = monthlyRows.reduce( (sum, row) => sum + weightValue(row) * wgtAdjValue(row), 0 ); const selectedWeight = monthlyRows .filter(row => statusValue(row) === selectedStatus) .reduce( (sum, row) => sum + weightValue(row) * wgtAdjValue(row), 0 ); const proportion = totalWeight > 0 ? selectedWeight / totalWeight : null; return { Year: year, Month: String(shipmentMonth).slice(4, 6), Status: "Sold/leased - residential", Proportion: proportion, Percentage: proportion === null ? "N/A" : proportion.toLocaleString("en-US", { style: "percent", minimumFractionDigits: 1, maximumFractionDigits: 1 }) }; })30:T562,{ const datasets = [ [2020, data2020], [2022, data2022], [2024, data2024] ]; const charts = datasets.map(([year, rows]) => { const data = rows .map(row => ({ Bedrooms: numberValue(row.BEDROOMS), SQFT: numberValue(row.SQFT) })) .filter(d => Number.isFinite(d.Bedrooms) && Number.isInteger(d.Bedrooms) && d.Bedrooms > 0 && d.Bedrooms !== 9 && Number.isFinite(d.SQFT) && d.SQFT > 0 ) return vl.markCircle({ size: 40, opacity: 0.3, color: '#0072B2' }) .data(data) .encode( vl.x().fieldO('Bedrooms') .sort('ascending') .axis({ title: 'Number of Bedrooms', labelExpr: "datum.value === 1 ? '≤2' : datum.value === 3 ? '<2' : datum.label", labelAngle: 0, titleFontSize: 14, labelFontSize: 12 }), vl.y().fieldQ('SQFT') .axis({ title: 'Home Area (sq ft)', titleFontSize: 14, labelFontSize: 12 }), vl.tooltip([ vl.fieldQ('Bedrooms'), vl.fieldQ('SQFT') ]) ) .title({text: String(year), fontSize: 18}) .width(250) .height(300); }); return vl.hconcat(...charts) .resolve({scale: {x: 'shared', y: 'shared'}}) .render(); 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25:{"id":"8369d5bb0578b595","slug":"assignment-2-manufactured-homes-in-the-united-states","title":"Assignment 2: Manufactured Homes in the United States","thumbnail":"6bb4830d3ad6b9385ca03a403d90e864aa62b5819552e1d848413d66d52a4536","access_level":"public","create_time":"$D2026-10-05T02:35:57.403Z","update_time":"$D2026-10-12T05:18:43.713Z","view_time":null,"first_public_time":"$D2026-10-12T05:19:54.801Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"d8ea6b0c2cceec70","login":"anna-elizabeth-morgan","name":"Anna Elizabeth Morgan","avatar_url":"https://avatars-next.observableusercontent.com/49a62e7514d81aee1558819416bf6935649f29d6e0ecab4533ffdeab48b1af0b","tier":"free_2026","bio":""},"version":1641,"publish_version":1641,"role":null,"authored":false,"authors":[{"type":"individual","id":"a964adcc11dfd433","login":"anna-elizabeth-morgan","name":"Anna Elizabeth Morgan","avatar_url":"https://avatars-next.observableusercontent.com/49a62e7514d81aee1558819416bf6935649f29d6e0ecab4533ffdeab48b1af0b","tier":null,"bio":""}],"members":[{"type":"team","id":"d8ea6b0c2cceec70","login":"anna-elizabeth-morgan","name":"Anna Elizabeth Morgan","avatar_url":"https://avatars-next.observableusercontent.com/49a62e7514d81aee1558819416bf6935649f29d6e0ecab4533ffdeab48b1af0b","tier":"free_2026","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Assignment 2: Manufactured Homes in the United States","cells":[{"id":1,"value":"# Assignment 2: Manufactured Homes in the United States","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"Manufactured homes currently make up 10% of newly built homes built in the United States. Their lower cost of production make them an attractive option in the increasingly unaffordable U.S. housing market. Until recently all manufactured homes have been required to maintain a steel chassis, used to transport the home, even after being placed in it's final destination. However a recent amendment to federal regulation removed this requirement allowing more versatility in this housing stock which has the potential to have a substantial impact in the manufactured home market (Egerter et al., 2026). Manufactured homes present an increasingly promising option to address a national housing shortage in times of sky high development costs. This report will explore characteristics and trends in US manufactured home sales since 2020 by visualizing data from the Manufactured Housing Survey conducted through the U.S. Census Bureau and how they may be impacted by new regulations","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":75,"value":"# Guiding Questions\n1. How have prices and sizes of manufactured homes changed over time?\n2. This new regulation also has the potential to have a zoning impact. Not requiring steel chassis can improve curb appeal, meaning more neighborhoods are willing to adopt them. In recent years what percentage of homes are placed in manufactured home communities versus other neighborhoods?\n3. Certain types of footings are easier to integrate with steel chassis. How does the type of footing a home is built with effect how likely it is to be sold?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":76,"value":"# The Dataset\nThe U.S. Census Burueau conducts a survey every year of HUD-inspected Manufactured Home Shipments. A variety of characteristics about the home itself and the location and state of the home four months after shipment are collected. This analysis explores data collected in the survey years 2020 through the most recent survey published in 2025.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":85,"value":"# Analysis","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":74,"value":"## Price and Size\nA primary benefit of manufactured homes is their lower cost compared to traditionally built homes. Therefore it was natural to first want to explore the price of these manufactured homes and how prices have changed over time. In the graph below which charts the average price and size of a manufactured home over time, you can clearly see a steep upward trend from 2020 to 2022 followed by a leveling off. After first graphing price, I then began to wonder if the rise from 2020 to 2022 was due primarily to inflation which was very high in this period, or if this could be due to a demand for larger homes following the pandemic where people were spending more time at home. I therefore decided to graph size along with price to see how these trends coincide.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":58,"value":"$27","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":77,"value":"### Conclusions and Observations\nFrom this graph you can see that as prices grew significantly from 2020 to 2021 most likely attributed to inflation while average size remained about the same. Then as prices continued to grow significantly in 2022 more buyers may have been forced to purchase smaller homes to keep their costs lower. After 2022 as prices leveled off, sizes of homes began steadly recovering to former levels.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":89,"value":"## Size and Bedrooms\nAfter analyzing overall trends in manufactured home size over time, I was also curious how closely tied size and number of bedrooms was. Where people planning smaller families due to budget constraints and therefore buying homes with less bedrooms or simply just buying homes with smaller living spaces? To study how closely tied these two variables were, I plotted a histogram of home area for homes with different numbers of bedrooms across three of the years studied. The database did not report exact number of bedrooms, but did report greater than two bedrooms or less than or equal to two bedrooms so these two categories are plotted for each year.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":68,"value":"$28","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":91,"value":"### Conclusions and Observations\nOne of the first observations from this plot was that there are significantly more manufactured homes that have greater than two bedrooms than those with less than or equal to two across all years. The histogram of greater than two bedroom homes also has two very distinct peaks around 1,400 and 1,800 square feet between all of the years. My initial suspicion is that this could be due to two very popular models of manufactured homes at these two specific sizes that drive up these frequency of those sizes, although I was unable to find enough information about specific models to confirm this. This plot also shows a right skew to both histograms with a smaller number of much larger homes in each bedroom category. Size and number of bedrooms overall seem to be correlated but these graphs reveal significant variability.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":78,"value":"## Price and Region\nAfter reviewing trends in over all manufactured home prices, I next wondered if this trend was consistent across all regions in the study to see if these prices were driven more my national inflation or regional factors.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":57,"value":"$29","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":80,"value":"### Conclusions and Observations\nGraphing the average prices of homes across regions showed that the upward trend from 2020 to 2022 followed by a leveling off of slight decrease remained remarkably consistent across regions. However, it did show the substantial offset of each region. For example while prices in the West and Midwest follow the same trends, the average in the west remains above the midwest by a constant of around $30,000","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"# Placement in Manufactured Home Communities\nNext I wanted to explore trends in manufactured home placement. The new regulation amendment to remove the requirement for a steel chassis has the potential to improve curb appeal of manufactored homes and potentially allow them into neighborhoods with HOA restrictions which have historically limited them. To understand what current trends in manufactored home placement, the graph below plots the proportions of manufactored homes placed in manufactored home communities from 2020 to 2025.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":65,"value":"{\n const data = communityProportionsByYear.filter(\n d => d.Year >= 2020 &&\n d.Year <= 2026 &&\n d.Category === \"Inside manufactured home community\"\n );\n\n return vl.markBar({opacity: 1})\n .data(data)\n .encode(\n vl.x().fieldO('Year')\n .axis({\n title: 'Year',\n labelAngle: 0,\n titleFontSize: 16,\n labelFontSize: 12\n }),\n\n vl.y().fieldQ('Proportion')\n .axis({\n title: 'Proportion In a Manufactured Community',\n format: '.0%',\n titleFontSize: 16,\n labelFontSize: 12\n })\n )\n .title({\n text: 'Proportion of Homes Placed In Manufactured Home Communities Over Time',\n fontSize: 20\n })\n .width(800)\n .height(300)\n .render();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":83,"value":"### Conclusions and Observations\nThe graph reveals an significant uptick in homes placed inside a manufactured home community from 2020 to 2021 followed by a steady decline back to 2020 levels. The may be a result of the sharp uptick in prices, pushing families to settle more in manufactored home communities which may offer a lower cost of living.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":84,"value":"# Home Titling\nThe analysis of price and placement trends seem to paint a picture of a market where manufactured home buyers are increasingly pinched by high prices from 2020 to 2022 before getting some relief as prices leveled in the following years. To confirm this picture that home buyers were more financially constrained around 2021 I also wanted to explore how trends in the titling of manufactured homes. A manufactured home titled private property means the home is movable and generally placed on leased land. A home titled as real estate on the other hand typically means that it is placed on land. While every situation is unique, a general trend towards personal property entitlements may be a demonstration of a more financially strained population.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":66,"value":"$2a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":86,"value":"### Conclusions and Observations\nInterestingly these graphs seem to show the opposite trend of what one may expect from the previous analysis on price and home placement. There is a general decrease in units titled as personal property as prices increased from 2020 to 2021 while those titled as real estate gradually increased. Also notable is that the number of homes not titled was also increasing during this time meaning a larger proportion of homes where not sold during these years. However, all of these changes are relatively minute, with a solid majority of 72% to 82% percent of units titled as personal property over all years analyzed.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":87,"value":"## Shipment Month and Likelihood of Sale\nThe analysis of home titling above revealed an increase in the number of homes which are not sold over time. This also made me wonder if there where trends within each of the years graphed of when homes are more or less likely to sell. To explore this I graphed the proportions of homes that are sold with in four months of shipment by the month that they are shipped for each year studied.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":59,"value":"$2b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":88,"value":"### Conclusions and Observations\nThis analysis showed that there is no consistent seasonal or monthly trend where homes are more and less likely to be sold across years. However, it did reveal other interest information about sales trends. First of all there is a clear downward trend since 2020 where homes where much more likely to sell within four months no matter the shipment months. While in all years this proportions remains remarkably close to 50% the majority of the time, there are some notably long dips in 2023, 2024 and 2025.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":93,"value":"## Footing and Likelihood of Sale\nNext I sought to answer my third guiding question. Footings which hold manufactured homes in place are likely to be substantial impacted by the recent regulation. While certain footing types like basements/crawl spaces can be very favorable for the extra space and can make pluming easier in climates where pipes freeze above the thaw line, but they are often much more difficult to integrate with the previously required steel chassis. Therefore, I wanted to understand in the current climate, which footing types are more likely to lead to a home selling in a timely manor. To answer this question I plotted the status of homes with different foot types after four months for 2025.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":69,"value":"$2c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":94,"value":"### Conclusions and Observations\nMost notably immediately is that the vast majority of all homes shipped in 2025 were not sold or leased after four months. While there are slight differences in home with different footings, footing type does not seem to have a major effect. All footing categories range within 12-22% of homes sold or leased within four months. Monolithic-slab foundations and concrete footings had the greatest proportions sold.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":96,"value":"## Footing Type and Region\nSince all footing types seemed equally likely to be sold within four months, I wondered what other factors may lead a manufacturer or home buyer to choose to build or purchase a home with a specific footing type. To explore further, I graphed the proportion of homes with different footing type in each of the regions included in the survey.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":70,"value":"$2d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":98,"value":"### Conclusions and Observations\nThis graph revealed clear regional trends in the types of footings used in manufactured homes. In the northeast monolithic slab foundations dominate. Upon further research I found these foundations are popular in snowy climates where they prevent water from freezing beneath the foundation (Snow & Sons LLC, n.d.). The footing category labeled as other makes up a large portion of the homes places in the south. While this makes specific conclusions more difficult to draw it is likely that footing types of homes in the south would more often need to consider the potential for flooding.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":99,"value":"# Works Cited\nEgerter, A., Hawke, S., & Metcalf, B. (2026, September 15). Beyond the chassis: State-level opportunities with the new manufactured housing definition from the ROAD to Housing Act. Terner Center for Housing Innovation.https://ternercenter.berkeley.edu/blog/beyond-the-chassis-state-level-opportunities-with-the-new-manufactured-housing-definition-from-the-road-to-housing-act/\n\nSnow & Sons LLC. (n.d.). Why monolithic slabs outperform traditional concrete foundations. https://www.snowandsonsllc.com/blog/why-monolithic-slabs-outperform-traditional-concrete-foundations\n\nU.S. Census Bureau. (n.d.). Manufactured Housing Survey public use file: 2024 [Data set]. https://www.census.gov/data/datasets/2021/econ/MHS/puf.html","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":92,"value":"### Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4,"value":"priceNumber = value =>\n value == null || value === \"\"\n ? NaN\n : Number(String(value).replace(/[$,\\s]/g, \"\"))\n","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":9,"value":"sqftNumber = value =>\n value == null || value === \"\"\n ? NaN\n : Number(String(value).replace(/[$,\\s]/g, \"\"))","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"validPrices = rows =>\n rows.map(row => priceNumber(row.PRICE)).filter(Number.isFinite)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":10,"value":"validSQFT = rows =>\n rows.map(row => priceNumber(row.SQFT)).filter(Number.isFinite)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"averageSQFT = rows =>\n ((sqft) =>\n sqft.length\n ? sqft.reduce((sum, sqft) => sum + sqft, 0) / sqft.length\n : null\n )(validSQFT(rows))","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"averagePrice = rows =>\n ((prices) =>\n prices.length\n ? prices.reduce((sum, price) => sum + price, 0) / prices.length\n : null\n )(validPrices(rows))","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2,"value":"$2e","pinned":false,"hidden":true,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":7,"value":"averagePriceByYear = datasetsByYear.map(([year, rows]) => {\n const eligibleRows = [1, 2, 3, 4].flatMap(regionCode =>\n eligibleRegionalRows(year, rows, regionCode)\n );\n\n return {\n Year: year,\n AveragePrice: weightedAveragePrice(eligibleRows),\n AverageSQFT: averageSQFT(rows)\n };\n})","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"Inputs.table(averagePriceByYear)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"value":"numberValue = value =>\n value == null || value === \"\"\n ? NaN\n : typeof value === \"number\"\n ? value\n : Number(String(value).replace(/[$,\\s]/g, \"\"))","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"datasetsByYear = [\n [2014, data2014],\n [2015, data2015],\n [2016, data2016],\n [2017, data2017],\n [2018, data2018],\n [2020, data2020],\n [2021, data2021],\n [2022, data2022],\n [2023, data2023],\n [2024, data2024],\n [2025, data2025]\n]","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":14,"value":"regionNames = new Map([\n [1, \"Northeast\"],\n [2, \"Midwest\"],\n [3, \"South\"],\n [4, \"West\"]\n])","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"statusValue = row =>\n numberValue(\n row.STATUS === undefined\n ? row.status\n : row.STATUS\n )","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":21,"value":"weightValue = row =>\n numberValue(\n row.WEIGHT === undefined\n ? row.weight\n : row.WEIGHT\n )","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"eligibleRegionalRows = (year, rows, regionCode) =>\n rows.filter(row =>\n numberValue(row.REGION) === regionCode &&\n (\n statusValue(row) === 1 ||\n statusValue(row) === 2\n )\n )","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":16,"value":"validPriceRows = rows =>\n rows.filter(row =>\n numberValue(row.PRICE) > 9 &&\n weightValue(row) > 0\n )","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"weightedAveragePrice = rows =>\n ((validRows) =>\n validRows.length\n ? validRows.reduce(\n (sum, row) =>\n sum +\n numberValue(row.PRICE) *\n weightValue(row),\n 0\n ) /\n validRows.reduce(\n (sum, row) => sum + weightValue(row),\n 0\n )\n : null\n )(validPriceRows(rows))","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"value":"regionalAveragePrices = datasetsByYear.flatMap(\n ([year, rows]) =>\n [1, 2, 3, 4].map(regionCode =>\n ((regionalRows) => ({\n Year: year,\n Region: regionNames.get(regionCode),\n AveragePrice: weightedAveragePrice(regionalRows),\n RecordsUsed: validPriceRows(regionalRows).length\n }))(\n eligibleRegionalRows(year, rows, regionCode)\n )\n )\n)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":23,"value":"wgtAdjValue = row =>\n numberValue(\n row.WGTADJ === undefined\n ? row.wgtadj\n : row.WGTADJ\n )","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":24,"value":"shipmentMonthValue = row =>\n numberValue(\n row.SHIPMONTH === undefined\n ? row.shipmonth\n : row.SHIPMONTH\n )","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"statusLabel = (year, code) =>\n year <= 2020\n ? ({\n 1: \"Intended for sale - residential\",\n 2: \"Placed/sold - residential\",\n 3: \"Nonresidential/other\"\n })[code]\n : ({\n 1: \"Sold/leased - residential\",\n 2: \"Intended for sale or lease - residential\",\n 3: \"Nonresidential/other\"\n })[code]","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":26,"value":"shipmentStatusRows = datasetsByYear.flatMap(([year, rows]) =>\n rows\n .filter(row =>\n statusValue(row) === (year <= 2020 ? 2 : 1)\n )\n .map(row => ({\n Year: year,\n ShipmentMonth: shipmentMonthValue(row),\n StatusCode: statusValue(row),\n Status: \"Sold/leased - residential\",\n EstimateWeight: weightValue(row) * wgtAdjValue(row)\n }))\n).filter(row =>\n Number.isFinite(row.ShipmentMonth) &&\n Number.isFinite(row.EstimateWeight) &&\n row.EstimateWeight > 0\n)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":60,"value":"Inputs.table(shipmentStatusRows)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"statusMonthKeys = Array.from(\n new Set(\n shipmentStatusRows.map(\n row => row.Year + \"|\" + row.ShipmentMonth\n )\n )\n).sort(\n (a, b) =>\n Number(a.split(\"|\")[1]) -\n Number(b.split(\"|\")[1])\n)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":28,"value":"weightedShipmentCount = rows =>\n rows.reduce(\n (sum, row) => sum + row.EstimateWeight,\n 0\n )","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"statusProportion = (year, shipmentMonth, statusCode) =>\n weightedShipmentCount(\n shipmentStatusRows.filter(row =>\n row.Year === year &&\n row.ShipmentMonth === shipmentMonth &&\n row.StatusCode === statusCode\n )\n ) /\n weightedShipmentCount(\n shipmentStatusRows.filter(row =>\n row.Year === year &&\n row.ShipmentMonth === shipmentMonth\n )\n )","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":30,"value":"statusProportionTable = statusMonthKeys.flatMap(key =>\n ((parts) =>\n [1, 2, 3].map(code => ({\n Year: parts[0],\n ShipmentMonth: parts[1],\n Month:\n String(parts[1]).slice(4, 6),\n StatusCode: code,\n Status: statusLabel(parts[0], code),\n Proportion: statusProportion(\n parts[0],\n parts[1],\n code\n )\n }))\n )(key.split(\"|\").map(Number))\n)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":61,"value":"$2f","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":32,"value":"Inputs.table(statusProportionDisplay)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":35,"value":"locationValue = row =>\n numberValue(\n row.LOCATION === undefined\n ? row.location\n : row.LOCATION\n )","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":36,"value":"finalDestinationValue = row =>\n numberValue(\n row.FINALDEST === undefined\n ? row.finaldest\n : row.FINALDEST\n )","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":37,"value":"isEligiblePlacement = (year, row) =>\n year <= 2020\n ? statusValue(row) === 2\n : (\n (\n statusValue(row) === 1 ||\n statusValue(row) === 2\n ) &&\n finalDestinationValue(row) === 1\n )","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":38,"value":"communityCategory = (year, row) =>\n year <= 2020\n ? (\n locationValue(row) === 1\n ? \"Inside manufactured home community\"\n : locationValue(row) === 3\n ? \"Outside manufactured home community\"\n : null\n )\n : (\n locationValue(row) === 1\n ? \"Inside manufactured home community\"\n : (\n locationValue(row) === 2 ||\n locationValue(row) === 3\n )\n ? \"Outside manufactured home community\"\n : null\n )","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":39,"value":"communityPlacementRows = datasetsByYear.flatMap(\n ([year, rows]) =>\n rows.map(row => ({\n Year: year,\n Category: communityCategory(year, row),\n EstimateWeight:\n weightValue(row) * wgtAdjValue(row),\n Eligible: isEligiblePlacement(year, row)\n }))\n).filter(row =>\n row.Eligible &&\n row.Category !== null &&\n Number.isFinite(row.EstimateWeight) &&\n row.EstimateWeight > 0\n)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":40,"value":"communityWeightTotal = rows =>\n rows.reduce(\n (sum, row) => sum + row.EstimateWeight,\n 0\n )","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":41,"value":"communityProportion = (year, category) =>\n communityWeightTotal(\n communityPlacementRows.filter(row =>\n row.Year === year &&\n row.Category === category\n )\n ) /\n communityWeightTotal(\n communityPlacementRows.filter(row =>\n row.Year === year\n )\n )","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":42,"value":"communityProportionsByYear = datasetsByYear.flatMap(\n ([year]) =>\n [\n \"Inside manufactured home community\",\n \"Outside manufactured home community\"\n ].map(category => ({\n Year: year,\n Category: category,\n Proportion: communityProportion(year, category)\n }))\n)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":43,"value":"communityProportionDisplay = communityProportionsByYear.map(\n row => ({\n Year: row.Year,\n Category: row.Category,\n Proportion: row.Proportion.toLocaleString(\"en-US\", {\n style: \"percent\",\n minimumFractionDigits: 1,\n maximumFractionDigits: 1\n })\n })\n)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":44,"value":"Inputs.table(communityProportionDisplay)","pinned":false,"hidden":true,"mode":"ts","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":47,"value":"titleValue = row =>\n numberValue(\n row.TITLED === undefined\n ? row.titled\n : row.TITLED\n )","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":49,"value":"titleCategory = row =>\n ({\n 1: \"Real estate\",\n 2: \"Personal property\",\n 3: \"Not titled\"\n })[titleValue(row)]","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":50,"value":"titleRows = datasetsByYear.flatMap(\n ([year, rows]) =>\n rows.map(row => ({\n Year: year,\n Category: titleCategory(row),\n EstimateWeight:\n weightValue(row) * wgtAdjValue(row),\n Eligible: isEligiblePlacement(year, row)\n }))\n).filter(row =>\n row.Eligible &&\n row.Category !== undefined &&\n Number.isFinite(row.EstimateWeight) &&\n row.EstimateWeight > 0\n)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":51,"value":"titleWeightTotal = rows =>\n rows.reduce(\n (sum, row) => sum + row.EstimateWeight,\n 0\n )","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":52,"value":"titleProportion = (year, category) =>\n titleWeightTotal(\n titleRows.filter(row =>\n row.Year === year &&\n row.Category === category\n )\n ) /\n titleWeightTotal(\n titleRows.filter(row =>\n row.Year === year\n )\n )","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":53,"value":"titleProportionsByYear = datasetsByYear.flatMap(\n ([year]) =>\n [\n \"Real estate\",\n \"Personal property\",\n \"Not titled\"\n ].map(category => ({\n Year: year,\n Category: category,\n Proportion: titleProportion(year, category)\n }))\n)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":54,"value":"titleProportionDisplay = titleProportionsByYear.map(\n row => ({\n Year: row.Year,\n Category: row.Category,\n Proportion: row.Proportion.toLocaleString(\"en-US\", {\n style: \"percent\",\n minimumFractionDigits: 1,\n maximumFractionDigits: 1\n })\n })\n)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":55,"value":"Inputs.table(titleProportionDisplay)","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":67,"value":"$30","pinned":false,"hidden":true,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W31","stdlib":"2"}} 26:"$25:body"