{"id":3645,"date":"2026-08-30T00:00:00","date_gmt":"2026-08-29T22:00:00","guid":{"rendered":"https:\/\/humes.pl\/how-to-research-and-forecast-the-construction-market-when-demand-depends-on-investment-cycles-and-public-funding\/"},"modified":"2026-08-25T15:56:30","modified_gmt":"2026-08-25T13:56:30","slug":"how-to-research-and-forecast-the-construction-market-when-demand-depends-on-investment-cycles-and-public-funding","status":"publish","type":"post","link":"https:\/\/humes.pl\/en\/how-to-research-and-forecast-the-construction-market-when-demand-depends-on-investment-cycles-and-public-funding\/","title":{"rendered":"How to research and forecast the construction market when demand depends on investment cycles and public funding"},"content":{"rendered":"<p>A building materials manufacturer planning volumes for the coming year and a contractor scheduling resources face the same problem: construction and assembly output data describe the past, while decisions must be made based on what is yet to enter the market. Reliable <strong>construction market forecasts<\/strong> therefore require working with leading sources &#8211; permits, tenders, order books, and investor declarations &#8211; rather than relying solely on completed output statistics. The research approach is outlined below: which data to collect, how to verify them, and how to combine them into a forecasting model.<\/p>\n<h2>How does construction market forecasting differ from other sectors?<\/h2>\n<p>Construction is among the industries characterized by strong cyclicality and often a long lag between an investment decision and project delivery. Several quarters or years may pass between submitting an application for a building permit and purchasing finishing materials. This means that three layers of demand coexist in the market at any given time: projects in preparation, projects under construction, and projects nearing completion. <strong>Construction market forecasts<\/strong> must distinguish between these layers because each generates demand for different groups of products and services.<\/p>\n<p>A second distinctive feature is the significant share of public and EU funding. In road, rail, water and wastewater, and energy infrastructure, market size is largely determined by the availability of funding, the pace at which it is contracted, and beneficiaries&#8217; capacity to absorb it. <a href=\"https:\/\/humes.pl\/en\/glossary\/desk-research\/\">Desk research<\/a> based on program schedules, contract registers, and tender procedure data can provide more insight here than extrapolating a time series alone.<\/p>\n<p>A third feature is the fragmented, multi-level decision-making structure. Depending on the segment and product, the choice of material is made by the investor, designer, general contractor, subcontractor, or distributor. Therefore, <strong>construction market research<\/strong> designed for forecasting requires identifying who actually controls the specification and including this group in the sample. Without this, a volume forecast will not translate into a sales forecast for a specific category.<\/p>\n<p>Finally, the market is highly regionally diversified. The residential segment is concentrated in the largest metropolitan areas, infrastructure is distributed according to project locations, and industrial construction follows the location decisions of major investors. <strong>Construction market analysis<\/strong> at the national level therefore masks differences that are crucial for logistics and distribution network planning.<\/p>\n<h2>How to build a forecast: data sources, methods, and sequence of steps<\/h2>\n<p>A practical forecasting model for construction is developed by combining secondary and primary data. The workflow in research projects usually follows the sequence below.<\/p>\n<p><strong>Step 1: mapping segments and units of measurement.<\/strong> The market must be broken down into segments with a common demand logic: multi-family residential, single-family residential, commercial (offices, retail, warehouses), industrial, linear infrastructure, point infrastructure, renovations, and upgrades. For each segment, a primary unit is defined &#8211; square meters of usable floor area, number of dwellings, kilometers, or contract value &#8211; along with a conversion factor into product units (e.g., material consumption per m\u00b2 or per dwelling).<\/p>\n<p><strong>Step 2: building a set of leading data.<\/strong> The most commonly used sources include:<\/p>\n<ul>\n<li>registers of building permits and notifications, as well as data on housing starts and completed dwellings (public statistics and administrative registers);<\/li>\n<li>databases of tender procedures and contract registers in public procurement &#8211; notices, awards, contract values, and completion dates;<\/li>\n<li>schedules and reports for programs funded by national and EU resources, including project lists and the status of contracting;<\/li>\n<li>stock exchange reports and financial statements of contracting companies and materials manufacturers &#8211; order books, backlog, margins, and signals of schedule shifts;<\/li>\n<li>data on material prices, labor costs, and price adjustment indices;<\/li>\n<li>business tendency surveys and construction sentiment indicators, including assessments of order books and business barriers.<\/li>\n<\/ul>\n<p><strong>Step 3: primary research to fill gaps.<\/strong> Secondary data do not answer questions about intentions, schedule shifts, actual progress of works, or material selection criteria. This is where <a href=\"https:\/\/humes.pl\/en\/glossary\/computer-assisted-telephone-interviewing-cati\/\">CATI<\/a> and CAWI interviews are used with samples of contractors, developers, designers, wholesalers, and retail outlets, alongside IDIs with decision-makers at the largest entities. A typical set of questions covers the value and structure of the order book, expected changes in volumes, risks of delays, inventory in the distribution channel, and the shares of specific solutions in projects being delivered.<\/p>\n<p><strong>Step 4: triangulation and balancing the equation.<\/strong> The forecast should be balanced from two sides: demand (the number and scale of projects multiplied by unit consumption) and supply (domestic production, imports, exports, and inventory changes). Manufacturers&#8217; sales and production capacity can serve as additional checks on the results. Discrepancies between these approaches are diagnostically valuable &#8211; they may indicate an underestimation of the renovation segment, unaccounted-for trade outside observed channels, or an incorrect unit conversion factor.<\/p>\n<p><strong>Step 5: scenarios rather than a single point estimate.<\/strong> Given the dependence on public decisions and financing costs, it is reasonable to present the outcome as three paths &#8211; baseline, slower contracting, and faster contracting &#8211; with assumptions explicitly stated: the pace of tender awards, interest rate levels, changes in material and labor costs, and worker availability. A change in an assumption should be immediately convertible into volume, which requires building a model rather than merely a results table.<\/p>\n<p>As Hume&#8217;s Institute experts point out, signals of changing market conditions in construction may emerge earlier in data on investments in preparation than in construction and assembly output statistics: &#8220;In Hume&#8217;s Institute projects, the leading signal is often observed at the stage of building permits and tender notices rather than in construction and assembly output data. When output statistics show a decline, decisions to limit new investments may have been made many months earlier and may be visible in permit registers and in the number and value of procedures being launched. Therefore, <strong>indicators for construction<\/strong> should be organized by their lead time rather than by data availability.&#8221;<\/p>\n<p><strong>Step 6: updating on a regular cycle.<\/strong> A construction forecast can become outdated quickly because a single major tender decision can change the outlook for a segment. In practice, the model is refreshed quarterly, with a short panel survey of contractors and distributors serving as a source of adjustments.<\/p>\n<h2>Which errors most often undermine construction market analysis?<\/h2>\n<p>Most unsuccessful forecasts in this sector result not from a lack of data but from design errors. Below are the pitfalls most frequently encountered in validation projects:<\/p>\n<ul>\n<li><strong>Extrapolating a trend in a cyclical sector.<\/strong> Extending the trend line from recent quarters works until the cycle changes phase &#8211; precisely when the forecast is needed. Models must account for leading variables and the structure of the cycle.<\/li>\n<li><strong>Confusing value with volume.<\/strong> When material and labor prices are rising sharply, growth in the value of output may coincide with a decline in volume. A manufacturer planning capacity needs volume, while the finance department needs value. Failing to separate these perspectives is a source of serious errors in <strong>construction market analysis<\/strong>.<\/li>\n<li><strong>Treating a permit as a certain project delivery.<\/strong> Some permits never result in construction or result in it only after a considerable delay. It is necessary to estimate the conversion rate and the distribution of delays, preferably by historically matching permits to construction starts in a given segment and region.<\/li>\n<li><strong>Overlooking the renovation and modernization segment.<\/strong> Renovation work, thermal upgrades, and finishing work often do not require permits, so they are incompletely represented in registers. However, for the <strong>building materials market<\/strong>, this is a significant part of demand, accessible mainly through distribution channel research and consumer research.<\/li>\n<li><strong>Ignoring channel inventory.<\/strong> Manufacturers&#8217; sales to wholesalers are not equivalent to consumption at construction sites. Without measuring channel inventory, the forecast confuses a demand signal with a stock-building signal &#8211; the classic bullwhip effect.<\/li>\n<li><strong>Segmentation that is too broad.<\/strong> A single indicator for &#8220;construction&#8221; combines segments with opposing cycles: public infrastructure and credit-financed residential construction respond to different stimuli and often at different times.<\/li>\n<li><strong>A non-representative sample on the contractor side.<\/strong> The contractor market is highly concentrated at the upper end and very fragmented at the lower end. A random sample without stratification by revenue size underestimates the share of large contracts, while a sample based solely on large entities overlooks single-family construction and renovations.<\/li>\n<\/ul>\n<p>An alternative to a model based on leading data is a purely econometric approach that links construction output to GDP, interest rates, and inflation. It is cheaper and faster, but performs poorly at turning points and in the public segment, where the program calendar rather than the macroeconomic cycle is decisive. In practice, the most effective approach is a combination: a macro model as the framework and data on permits, tenders, and order books as a short-term adjustment. This setup also makes it possible to clearly indicate which <strong>construction market forecasts<\/strong> are based on hard registers and which rely on respondent declarations.<\/p>\n<h2>What should be included in a set of indicators for construction?<\/h2>\n<p>Below is a summary organizing <strong>indicators for construction<\/strong> by lead-time horizon. This structure makes it possible to quickly assess whether the data set used by a company actually has forecasting capability or merely describes past conditions:<\/p>\n<ol>\n<li><strong>Leading by several quarters or years:<\/strong> number and floor area of properties covered by building permits, applications for development conditions, notices of tender procedures, the status of public funding contracted under programs, and location decisions by major industrial investors.<\/li>\n<li><strong>Leading by several months:<\/strong> tender awards and signed contracts, contractor backlog, housing starts, developer home sales, lending activity and the number of mortgage applications, and assessments of order books in business tendency surveys.<\/li>\n<li><strong>Current:<\/strong> construction and assembly output, building materials manufacturers&#8217; sales, production capacity utilization, employment and vacancies in construction, material prices and labor rates, and wholesale inventory levels.<\/li>\n<li><strong>Lagging, useful for calibration:<\/strong> completed dwellings, value of completed works, and data on bankruptcies and payment backlogs in the sector.<\/li>\n<\/ol>\n<p>The set is supplemented with primary data from B2B research: declared purchasing plans, anticipated volume changes, expected schedule shifts, and criteria for selecting suppliers and materials. Combining public registers with a panel of industry respondents produces a forecast that can be evaluated &#8211; meaning it is possible to verify afterward which assumption failed.<\/p>\n<h2>Frequently asked questions<\/h2>\n<h3>What data describe the construction market?<\/h3>\n<p>The construction market is described simultaneously from four perspectives: volume (number and floor area of properties, kilometers of infrastructure), value (value of output and contracts), resources (employment, production capacity, equipment), and costs (material prices, labor rates). The basis consists of public statistics, permit registers, public procurement databases, and company reports. These data are supplemented with primary research among contractors, designers, and distributors because registers do not fully cover the renovation segment or decisions regarding material specifications.<\/p>\n<h3>How can demand in construction be forecast?<\/h3>\n<p>A forecast is developed by translating the number and scale of projects in preparation into unit consumption of materials and labor, taking into account the conversion rate from permits to construction starts and the distribution of delays. The result is balanced from the supply side by comparing it with domestic production, imports, exports, and changes in channel inventory, while manufacturers&#8217; sales are used as an additional checkpoint. Given the impact of public decisions and financing costs, the forecast is presented in scenarios with explicit assumptions and updated quarterly.<\/p>\n<h3>Which indicators lead construction market conditions?<\/h3>\n<p>Data on building permits, tender notices, and the status of public funding contracted may provide the greatest lead time. Over the medium term, signals are provided by tender awards, contractor backlog, the number of housing starts, developer home sales, and lending activity. Construction and assembly output data are current indicators, while data on completed properties are lagging and serve primarily to calibrate the model rather than detect turning points.<\/p>\n<h2>Ask about research or a forecast for the construction market<\/h2>\n<p>Hume&#8217;s Institute designs construction market research and forecasting models that combine public registers, tender data, and primary research among contractors, designers, and distributors. <a href=\"https:\/\/humes.pl\/en\/contact\/\">Contact us<\/a> to discuss the data scope and methodology tailored to your segment.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A building materials manufacturer planning volumes for the coming year and a contractor scheduling resources face the same problem: construction and assembly output data describe the past, while decisions must be made based on what is yet to enter the market. Reliable construction market forecasts therefore require working with leading sources &#8211; permits, tenders, order [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":3651,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[912],"tags":[],"slowa_kluczowe":[],"class_list":["post-3645","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-badania-i-analizy"],"acf":[],"_wp_attached_file":null,"_wp_attachment_metadata":null,"wpml_media_processed":null,"_wpml_media_usage_in_posts":null,"_wp_attachment_context":null,"_oembed_35c905c64c03156f243b94f18c4eb80f":null,"_wp_attachment_image_alt":null,"rank_math_description":"Learn how to forecast the construction market using permits, tenders and order books as leading indicators instead of past output statistics.","rank_math_focus_keyword":"forecast the construction market","rank_math_contentai_score":null,"_wpml_post_translation_editor_native":null,"_menu_item_type":null,"_menu_item_menu_item_parent":null,"_menu_item_object_id":null,"_menu_item_object":null,"_menu_item_target":null,"_menu_item_classes":null,"_menu_item_xfn":null,"_menu_item_url":null,"_wp_page_template":null,"rank_math_og_content_image":null,"_wp_trash_meta_status":null,"_wp_trash_meta_time":null,"_wp_desired_post_slug":null,"rank_math_primary_category":910,"_acf_changed":null,"wp_pattern_sync_status":null,"_form":null,"_mail":null,"_mail_2":null,"_messages":null,"_additional_settings":null,"_locale":null,"_hash":null,"_config_validation":null,"_wp_old_slug":null,"rank_math_internal_links_processed":"1","_top_nav_excluded":null,"_cms_nav_minihome":null,"_thumbnail_id":"3651","_last_translation_edit_mode":null,"_wpml_word_count":"2349","_dp_original":null,"_edit_last":null,"_edit_lock":null,"rank_math_seo_score":null,"_wpml_location_migration_done":null,"_wpml_media_duplicate":null,"_wpml_media_featured":null,"_wp_old_date":"2026-08-25","copied_media_ids":[],"referenced_media_ids":[],"rank_math_title":"How to forecast the construction market | Hume's Institute","job_department":null,"_job_department":null,"job_location":null,"_job_location":null,"job_offer_external_link":null,"_job_offer_external_link":null,"footnotes":null,"inline_featured_image":null,"blog_podtytul":null,"_blog_podtytul":null,"blog_czas_czytania":null,"_blog_czas_czytania":null,"blog_dalsza_lektura":null,"_blog_dalsza_lektura":null,"slownik_krotka_definicja":null,"_slownik_krotka_definicja":null,"slownik_cytat":null,"_slownik_cytat":null,"slownik_na_stronie_glownej":"1","_slownik_na_stronie_glownej":null,"slownik_slowa_kluczowe":null,"_slownik_slowa_kluczowe":null,"slownik_w_praktyce":null,"_slownik_w_praktyce":null,"slownik_powiazane":null,"_slownik_powiazane":null,"slownik_kluczowe_punkty":null,"_slownik_kluczowe_punkty":null,"lang":"en","translations":{"en":3645},"pll_sync_post":{},"_links":{"self":[{"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/posts\/3645","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/comments?post=3645"}],"version-history":[{"count":1,"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/posts\/3645\/revisions"}],"predecessor-version":[{"id":3646,"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/posts\/3645\/revisions\/3646"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/media\/3651"}],"wp:attachment":[{"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/media?parent=3645"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/categories?post=3645"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/tags?post=3645"},{"taxonomy":"slowa_kluczowe","embeddable":true,"href":"https:\/\/humes.pl\/en\/wp-json\/wp\/v2\/slowa_kluczowe?post=3645"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}