Analysis of Construction Cost and Investment Planning Using Time Series Data
Abstract
:1. Introduction
2. Materials and Methods
Factors of Construction Cost Prediction | References | |
---|---|---|
Economic growth, Construction cost index | Acquisition of rights to land and building | [20] |
Types of work (general cost, installation works, engineering works, etc.) Construction side location (in the city center, outside the city center, non-urban, etc.) | The overall duration of the construction works, Size and necessary potential of the main contractor | [19] |
Consumer price index, employment level in construction, Building permits, Money supply | Crude oil prices, Producer price index Housing starts, Gross domestic product | [18] |
Consumer price index, Federal funds rate, Unemployment rate, The employment rate in construction, Average weekly hours, Prime loan rate | Building permits, Money supply, Average hourly earnings, Crude oil price, Housing starts, Construction spending, Gross domestic product | [16] |
The selling price of residential properties, Total transaction of residential properties, | Total number of residences, Total population, Total number of new mortgages | [26] |
Consumer price index, crude oil price, producer price index, gross domestic product, | employment levels, number of building permits, the number of housings starts the money supply crude oil prices | [27] |
Payment delay by the client, Change by the client during construction, Owner understanding and granting strategy, Estimator’s experience level, | Estimation methods, Techniques used, Location of project, Quality and contents of specification code | [28] |
Clear and detailed drawings, Experience and skill level of estimators, Materials (price, availability, quality), Experience on similar projects, | Accuracy of the bill of quantities, Management team, Financial capacity, Quality of assumption, Project complexity of design, | [29] |
3. Results and Discussion
3.1. Trend of Construction Cost Index
3.2. Descriptive Analysis and Pearson Correlation (I) of the Original Variables
3.3. Forecast of CCI Using Holt Model
3.4. Forecast of Independent Variables for September 2021 to December 2022
3.5. Pearson Correlation (II) of Forecasted Independent and Dependent Variables
3.6. Impact of Transportation Metrics on the Construction Cost Index
3.7. Forecast of Transportation Metric
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
- Construction Cost Index—https://www.enr.com/economics (accessed on 30 December 2021)
- Consumer Price Index—https://www.bls.gov/cpi/ (accessed on 30 December 2021)
- Unemployment Rate (general)—https://www.bls.gov/eag/eag.us.htm (accessed on 30 December 2021)
- Employment Rate (general)—https://www.bls.gov/ces/ (accessed on 30 December 2021)
- Producer Price Index—https://www.bls.gov/ppi/ (accessed on 30 December 2021)
- Crude Oil Prices—https://www.eia.gov/outlooks/steo/report/prices.php (accessed on 30 December 2021)
- Gross Domestic Product—https://www.bea.gov/data/gdp/gross-domestic-product (accessed on 30 December 2021)
- Building Permits—https://socds.huduser.gov/permits/ (accessed on 30 December 2021)
- US Import Price Index—https://www.bls.gov/mxp/ (accessed on 30 December 2021)
- Money supply—https://www.federalreserve.gov/releases/h6/current/default.htm (accessed on 30 December 2021)
Conflicts of Interest
References
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Dependent Variable | Source |
---|---|
Construction Cost Index (CCI) | Engineering News-Record [30] |
Independent Variables | |
Consumer Price Index (CPI) | U.S. Bureau of Labor Statistics [31] |
Unemployment Rate (UNEMP) (general) | U.S. Bureau of Labor Statistics [31] |
Employment Rate (EMP) (general) | U.S. Bureau of Labor Statistics [31] |
Producer Price Index (PPI) | U.S. Bureau of Labor Statistics [31] |
Crude Oil Prices (COIL) * | U.S. Energy Information Administration [32] |
Gross Domestic Products (GDP) | Bureau of Economic Analysis [33] |
Building Permits (BP) | Census Bureau [34], Housing and Urban Development [35] |
Import Price Index (IPI) | U.S. Bureau of Labor Statistics [31] |
Money Supply (MS) * | U.S. Board of Governors of the Federal Reserve System [36] |
Name | Mean | Sth. Dev. | Coefficient of Variation | Name | Mean | Sth. Dev. | Coefficient of Variation |
---|---|---|---|---|---|---|---|
CCI | 11,518.4063 | 308.5893 | 2.6791 | COIL | 52.0747 | 12.8476 | 24.6715 |
BP | 123,176.5000 | 18,290.1588 | 14.8487 | MS | 17,235.2344 | 2297.0631 | 13.3277 |
CPI | 259.8683 | 5.4227 | 2.0867 | PPI | 119.9594 | 3.2528 | 2.7116 |
UNEMP | 59.6619 | 4.5107 | 7.5605 | GDP | 2.4750 | 2.0947 | 84.6339 |
EMP | 5.8438 | 2.9303 | 50.1431 | IPI | 125.8156 | 4.0993 | 3.2582 |
CCI | BP | CPI | UNEMP | EMP | COIL | MS | PPI | GDP | IPI | ||
---|---|---|---|---|---|---|---|---|---|---|---|
CCI | Pearson | 1 | 0.726 ** | 0.976** | −0.706 ** | 0.104 | 0.398 * | 0.853 ** | 0.953 ** | 0.738 ** | 0.727 ** |
p | <0.001 | <0.001 | <0.001 | 0.571 | 0.024 | <0.001 | <0.001 | <0.001 | <0.001 | ||
BP | Pearson | 0.726 ** | 1 | 0.794 ** | −0.519 ** | 0.014 | 0.405 * | 0.742 ** | 0.757 ** | 0.767 ** | 0.628 ** |
p | <0.001 | <0.001 | 0.002 | 0.939 | 0.021 | <0.001 | <0.001 | <0.001 | <0.001 | ||
CPI | Pearson | 0.976 ** | 0.794 ** | 1 | −0.690 ** | 0.030 | 0.455 ** | 0.858 ** | 0.962 ** | 0.818 ** | 0.762 ** |
p | <0.001 | <0.001 | <0.001 | 0.872 | 0.009 | <0.001 | <0.001 | <0.001 | <0.001 | ||
UNEMP | Pearson | −0.706 ** | −0.519 ** | −0.690 ** | 1 | −0.091 | −0.369 * | −0.688 ** | −0.729 ** | −0.657 ** | −0.637 ** |
p | <0.001 | 0.002 | <0.001 | 0.620 | 0.038 | <0.001 | <0.001 | <0.001 | <0.001 | ||
EMP | Pearson | 0.104 | 0.014 | 0.030 | −0.091 | 1 | −0.710 ** | 0.433 * | −0.133 | −0.283 | −0.472 ** |
p | 0.571 | 0.939 | 0.872 | 0.620 | <0.001 | 0.013 | 0.468 | 0.117 | 0.006 | ||
COIL | Pearson | 0.398 * | 0.405 * | 0.455 ** | −0.369 * | −0.710 ** | 1 | 0.071 | 0.627 ** | 0.639 ** | 0.885 ** |
p | 0.024 | 0.021 | 0.009 | 0.038 | <0.001 | 0.699 | <0.001 | <0.001 | <0.001 | ||
MS | Pearson | 0.853 ** | 0.742 ** | 0.858 ** | −0.688 ** | 0.433 * | 0.071 | 1 | 0.740 ** | 0.644 ** | 0.436 * |
p | <0.001 | <0.001 | <0.001 | <0.001 | 0.013 | 0.699 | <0.001 | <0.001 | 0.013 | ||
PPI | Pearson | 0.953 ** | 0.757 ** | 0.962 ** | −0.729 ** | −0.133 | 0.627 ** | 0.740 ** | 1 | 0.838 ** | 0.889 ** |
p | <0.001 | <0.001 | <0.001 | <0.001 | 0.468 | <0.001 | <0.001 | <0.001 | <0.001 | ||
GDP | Pearson | 0.738 ** | 0.767 ** | 0.818 ** | −0.657 ** | −0.283 | 0.639 ** | 0.644 ** | 0.838 ** | 1 | 0.738 ** |
p | <0.001 | <0.001 | <0.001 | <0.001 | 0.117 | <0.001 | <0.001 | <0.001 | <0.001 | ||
IPI | Pearson | 0.727 ** | 0.628 ** | 0.762 ** | −0.637 ** | −0.472 ** | 0.885 ** | 0.436 * | 0.889 ** | 0.738 ** | 1 |
p | <0.001 | <0.001 | <0.001 | <0.001 | 0.006 | <0.001 | 0.013 | <0.001 | <0.001 |
Fit Statistic | Value | Fit Statistic | Value | Fit Statistic | Value |
---|---|---|---|---|---|
Stationary R-squared | 0.410 | MAPE | 0.217 | MaxAE | 194.060 |
R-squared | 0.980 | MaxAPE | 1.619 | Normalized BIC | 7.789 |
RMSE | 44.098 | MAE | 25.394 |
MONTH | YEAR | PREDICTED | LCL | UCL | MONTH | YEAR | PREDICTED | LCL | UCL |
---|---|---|---|---|---|---|---|---|---|
September | 2021 | 12,635.03 | 12,544.97 | 12,725.09 | May | 2022 | 14,181.19 | 13,284.05 | 15,078.33 |
October | 2021 | 12,828.30 | 12,689.25 | 12,967.34 | June | 2022 | 14,374.46 | 13,332.63 | 15,416.29 |
November | 2021 | 13,021.57 | 12,810.42 | 13,232.71 | July | 2022 | 14,567.73 | 13,374.05 | 15,761.41 |
December | 2021 | 13,214.84 | 12,915.72 | 13,513.95 | August | 2022 | 14,761.00 | 13,408.65 | 16,113.36 |
January | 2022 | 13,408.11 | 13,008.60 | 13,807.62 | September | 2022 | 14,954.27 | 13,436.71 | 16,471.83 |
February | 2022 | 13,601.38 | 13,090.89 | 14,111.87 | October | 2022 | 15,147.54 | 13,458.50 | 16,836.59 |
March | 2022 | 13,794.65 | 13,163.72 | 14,425.58 | November | 2022 | 15,340.81 | 13,474.24 | 17,207.39 |
April | 2022 | 13,987.92 | 13,227.91 | 14,747.93 | December | 2022 | 15,534.08 | 13,484.13 | 17,584.03 |
ID | Description | Type | ID | Description | Type |
---|---|---|---|---|---|
BP | Model_1 | Winter’s Additive | COIL | Model_5 | ARIMA (0, 1, 1) |
CPI | Model_2 | ARIMA (0, 2, 0) | MS | Model_6 | ARIMA (0, 2, 0) |
UNEMP | Model_3 | Simple Seasonal | PPI | Model_7 | ARIMA (0, 2, 1) |
EMP | Model_4 | Simple Seasonal | GDP | Model_8 | ARIMA (3, 1, 0) |
IPI | Model_9 | ARIMA (0, 2, 0) |
Model | Model Fit Statistics | Ljung-Box Q (18) | |||
---|---|---|---|---|---|
Stationary R-Squared | R-Squared | Statistics | DF | Sig. | |
BP-Model_1 | 0.707 | 0.751 | 23.658 | 15 | 0.071 |
CPI-Model_2 | −2.022 × 10−17 | 0.984 | 22.999 | 18 | 0.191 |
UNEMP-Model_3 | 0.782 | 0.736 | 16.428 | 16 | 0.424 |
EMP-Model_4 | 0.721 | 0.690 | 26.935 | 16 | 0.042 |
COIL-Model_5 | 0.285 | 0.823 | 10.639 | 17 | 0.875 |
MS-Model_6 | 2.011 × 10−16 | 0.993 | 21.116 | 18 | 0.274 |
PPI-Model_7 | 0.274 | 0.974 | 7.450 | 17 | 0.977 |
GDP-Model_8 | 0.249 | 0.737 | 10.982 | 17 | 0.857 |
IPI-Model_9 | 0.000 | 0.934 | 15.416 | 18 | 0.633 |
Fit Statistic | Mean | SE | Minimum | Maximum | Percentile | ||
---|---|---|---|---|---|---|---|
5 | 90 | 95 | |||||
Stationary R-squared | 0.335 | 0.324 | −2.022 × 10−17 | 0.782 | −2.220 × 10−16 | 0.782 | 0.782 |
R-squared | 0.847 | 0.124 | 0.690 | 0.993 | 0.690 | 0.993 | 0.993 |
RMSE | 1070.384 | 3134.629 | 0.529 | 9427.694 | 0.529 | 9427.694 | 9427.694 |
MAPE | 5.519 | 6.446 | 0.193 | 17.605 | 0.193 | 17.605 | 17.605 |
MaxAPE | 44.625 | 67.704 | 0.615 | 203.457 | 0.615 | 203.457 | 203.457 |
MAE | 798.485 | 2344.909 | 0.411 | 7050.581 | 0.411 | 7050.581 | 7050.581 |
MaxAE | 2672.519 | 7793.375 | 1.167 | 23,449.075 | 1.167 | 23,449.075 | 23,449.075 |
Normalized BIC | 3.847 | 6.582 | −1.160 | 18.628 | −1.160 | 18.628 | 18.628 |
Month | Year | BP | CPI | EMP | UNEMP | COIL | MS | PPI | GDP | IPI |
---|---|---|---|---|---|---|---|---|---|---|
Sept. | 2021 | 150,207.64 | 273.77 | 56.13 | 4.95 | 65.24 | 21,067.91 | 130.00 | 5.70 | 133.74 |
Oct. | 2021 | 158,415.65 | 274.53 | 56.34 | 4.55 | 65.24 | 21,347.03 | 131.11 | 5.89 | 133.23 |
Nov. | 2021 | 139,852.66 | 275.31 | 56.25 | 4.45 | 65.24 | 21,364.36 | 132.21 | 5.89 | 132.66 |
Dec. | 2021 | 146,541.17 | 276.11 | 56.21 | 4.45 | 65.24 | 21,929.90 | 133.32 | 5.89 | 132.03 |
Jan. | 2022 | 146,289.07 | 276.91 | 49.60 | 3.90 | 65.24 | 22,233.65 | 134.42 | 5.80 | 131.35 |
Feb. | 2022 | 137,591.75 | 277.75 | 53.54 | 3.77 | 65.24 | 22,545.61 | 135.53 | 5.80 | 130.61 |
Mar. | 2022 | 160,649.10 | 278.60 | 53.37 | 4.00 | 65.24 | 22,865.78 | 136.63 | 5.80 | 129.81 |
April | 2022 | 158,432.77 | 279.47 | 52.50 | 7.40 | 65.24 | 23,194.16 | 137.73 | 5.80 | 128.96 |
May | 2022 | 157,918.78 | 280.36 | 52.84 | 6.80 | 65.24 | 23,530.75 | 138.84 | 5.84 | 128.05 |
June | 2022 | 164,083.79 | 281.29 | 53.01 | 6.10 | 65.24 | 23,875.55 | 139.94 | 5.84 | 127.08 |
July | 2022 | 165,363.12 | 282.24 | 53.18 | 5.63 | 65.24 | 24,228.56 | 141.05 | 5.84 | 126.06 |
Aug. | 2022 | 170,432.11 | 283.22 | 53.42 | 5.00 | 65.24 | 24,589.21 | 142.15 | 5.82 | 124.98 |
Sept. | 2022 | 166,520.26 | 284.23 | 56.13 | 4.95 | 65.24 | 24,989.21 | 143.26 | 5.82 | 123.84 |
Aug. | 2022 | 174,728.26 | 285.28 | 56.34 | 4.55 | 65.24 | 25,336.85 | 144.36 | 5.83 | 122.65 |
Nov. | 2022 | 156,165.28 | 286.37 | 56.25 | 4.45 | 65.24 | 25,722.70 | 145.47 | 5.83 | 121.40 |
Dec. | 2022 | 162,853.79 | 287.50 | 56.21 | 4.45 | 65.24 | 26,116.76 | 146.57 | 5.83 | 120.09 |
Name | CCI | CPI Transport | GDP Transport | TSI Freight | |
---|---|---|---|---|---|
CCI | Pearson Correlation | 1 | 0.257 | −0.213 | −0.055 |
Sig. (2−tailed) | 0.156 | 0.242 | 0.767 | ||
CPI Transport | Pearson Correlation | 0.257 | 1 | −0.158 | 0.792 |
Sig. (2−tailed) | 0.156 | 0.387 | <0.001 | ||
GDP Transport | Pearson Correlation | −0.213 | −0.158 | 1 | 0.075 |
Sig. (2−tailed) | 0.242 | 0.387 | 0.683 | ||
TSI Freight | Pearson Correlation | −0.055 | 0.792 | 0.075 | 1 |
Sig. (2−tailed) | 0.767 | <0.001 | 0.683 |
Fit Statistic | Mean | SE | Min. | Max. | Percentile | |||
---|---|---|---|---|---|---|---|---|
5 | 10 | 90 | 95 | |||||
Stationary R-squared | 0.091 | 0.140 | −0.001 | 0.252 | −0.001 | −0.001 | 0.252 | 0.252 |
R-squared | 0.478 | 0.223 | 0.252 | 0.699 | 0.252 | 0.252 | 0.699 | 0.699 |
RMSE | 8.049 | 7.867 | 2.466 | 17.047 | 2.466 | 2.466 | 17.047 | 17.047 |
MAPE | 157.810 | 271.572 | 0.824 | 471.394 | 0.824 | 0.824 | 471.394 | 471.394 |
MaxAPE | 2676.961 | 4626.141 | 4.157 | 8018.768 | 4.157 | 4.157 | 8018.768 | 8018.768 |
MAE | 5.222 | 5.332 | 1.611 | 11.346 | 1.611 | 1.611 | 11.346 | 11.346 |
MaxAE | 26.996 | 26.937 | 10.002 | 58.055 | 10.002 | 10.002 | 58.055 | 58.055 |
Normalized BIC | 3.623 | 1.972 | 1.914 | 5.780 | 1.914 | 1.914 | 5.780 | 5.780 |
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Jiang, F.; Awaitey, J.; Xie, H. Analysis of Construction Cost and Investment Planning Using Time Series Data. Sustainability 2022, 14, 1703. https://doi.org/10.3390/su14031703
Jiang F, Awaitey J, Xie H. Analysis of Construction Cost and Investment Planning Using Time Series Data. Sustainability. 2022; 14(3):1703. https://doi.org/10.3390/su14031703
Chicago/Turabian StyleJiang, Fengchang, John Awaitey, and Haiyan Xie. 2022. "Analysis of Construction Cost and Investment Planning Using Time Series Data" Sustainability 14, no. 3: 1703. https://doi.org/10.3390/su14031703